diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6 Binary files /dev/null and b/.DS_Store differ diff --git a/README.md b/README.md index 75d823e924f49e241b7b5f1143696bd98de2afe3..3ea5642640b85ec42d9420b45f73b32dc78a7b9d 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,138 @@ +# Emotion Classifier (NLP) + +A simple NLP-based emotion classification app that uses a fine-tuned transformer model on the GoEmotions dataset to predict the emotion conveyed in a given sentence. + +## Project Structure + +Emotion-Classifier-NLP/ +├── notebooks/ +│ ├── 01_exploration.ipynb +│ ├── 02_training.ipynb +│ ├── 03_evaluation.ipynb +│ └── 04_comparison.ipynb +├── src/ +│ ├── data_loader.py +│ ├── model.py +│ ├── model_hartmann.py +│ ├── model_custom.py +│ ├── train.py +│ └── evaluate.py +├── app/ +│ └── app.py +├── outputs/ +│ ├── model/ # Trained model +│ ├── metrics/ # Evaluation metrics +│ └── interpretations/ # Integrated gradients visualization plots +├── requirements.txt +├── README.md +└── .gitignore + +## Features +- Uses Hugging Face Transformers with a fine-tuned model +- Classifies emotion from text across 28 GoEmotions labels +- Streamlit frontend for interactive use +- Displays model prediction probabilities +- Shows sample integrated gradients visualizations per label (optional) + +## Running the App Locally +### Make sure you have Streamlit and other dependencies installed: + +pip install -r requirements.txt + +### Then start the app using: + +streamlit run app/app.py + +## Running the App on a web browser + +You can visit https://emotion-classifier-nlp.streamlit.app/ to run the app online + +## Example Output +- Input: "I find this funny" +- Output: Predicted Emotion: `amusement` +- Shows prediction probabilities across all 28 classes + +## Notes +- Pretrained model is saved in `outputs/model/` +- Integrated Gradients plots should be saved under `outputs/interpretations/` and named using the format: `sample_{n}_{label}.png` + + +# Performance Metrics + +| Metric | Score | +|------------|-------| +| Accuracy | 60.2% | +| Macro F1 | 48.3% | +| Weighted F1| 59.6% | + +### Confusion matrix + +![confusion_matrix](https://github.com/user-attachments/assets/f571bafa-daa9-4cf2-88cd-2bad069d187a) + + + +# Model Comparison (Hartmann vs Custom Model) + +| Sample Sentence | Hartmann Prediction(s) | Custom Model Prediction(s) | +|-----------------------------------------------------|-----------------------------|-----------------------------| +| I love spending time with my family. | joy, sadness, disgust | love, joy, admiration | +| This is the worst day of my life. | disgust, anger, sadness | anger, surprise, disgust | +| I'm feeling very nervous about the exam. | fear, sadness, joy | nervousness, fear, embarrassment | +| What a beautiful sunset! | joy, surprise, neutral | admiration, excitement, joy | +| I feel so disappointed and frustrated. | sadness, anger, disgust | disappointment, annoyance, anger | +| I'm not sure how to feel about this. | neutral, disgust, sadness | confusion, optimism, disapproval | +| That was hilarious, I can't stop laughing! | joy, surprise, neutral | amusement, joy, optimism | +| I feel completely empty and lost. | sadness, neutral, disgust | surprise, disappointment, optimism | + +**Insights**: +- The custom model captured more nuanced emotions, while Hartmann’s model tended to favor high-level emotions. +- Some variance due to differences in label granularity between the models. +- The custom model showed stronger performance in emotions like admiration, amusement, and disappointment. + +--- + +# Known Limitations + +- **Single-label restriction**: While the data supports multi-label emotion classification, the model currently predicts only the highest probability emotion. +- **Low support for some classes**: Emotions like grief and pride had low representation in the training data. +- **Data bias**: Results reflect Reddit comment biases present in the GoEmotions dataset. + --- -title: Emotion Classifier Nlp -emoji: 📚 -colorFrom: indigo -colorTo: gray -sdk: gradio -sdk_version: 5.29.0 -app_file: app.py -pinned: false + +# Confidence Threshold + +A **confidence threshold of 0.6** is applied in the app. +If the top emotion’s probability is below this value, the app returns: + +"Unclear / Not enough signal" + +This prevents overconfident predictions on uncertain or ambiguous text. + --- -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference +# Future Work + +- Expand to multi-label predictions to better capture complex emotions. +- Improve minority class performance via data augmentation or rebalancing. +- Incorporate explainability methods directly into the Streamlit app. +- Deploy the app to Streamlit Cloud or Hugging Face Spaces. +- Collect user feedback for real-world validation. + +--- + +# Credits + +- **Model architecture**: [RoBERTa](https://huggingface.co/roberta-base) (Hugging Face) +- **Training dataset**: [GoEmotions](https://huggingface.co/datasets/go_emotions) +- **Reference model**: [Hartmann et al. (2023)](https://arxiv.org/abs/2305.05894) +- **Streamlit App Framework**: [Streamlit](https://streamlit.io/) +- **Berta Emotion Model**: [BERTa](https://huggingface.co/bhadresh-savani/bert-base-uncased-emotion) +- **Transformers Library**: [Hugging Face Transformers](https://huggingface.co/docs/transformers/index) + +--- + +# License + +This project is licensed under the MIT License. +You can use, modify, and distribute the software freely, but there is no warranty. + diff --git a/app/.DS_Store b/app/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6 Binary files /dev/null and b/app/.DS_Store differ diff --git a/app/app.py b/app/app.py new file mode 100644 index 0000000000000000000000000000000000000000..53e2e294af8091b93ec063ab01f7ad624cd0be6a --- /dev/null +++ b/app/app.py @@ -0,0 +1,62 @@ +import os +os.system("git lfs pull") + +import streamlit as st +import torch +from transformers import AutoTokenizer, AutoModelForSequenceClassification +import numpy as np + +st.set_page_config(page_title="Emotion Classifier", layout="centered") + +# Load GoEmotions label names +GOEMOTIONS_LABELS = [ + "admiration", "amusement", "anger", "annoyance", "approval", "caring", + "confusion", "curiosity", "desire", "disappointment", "disapproval", + "disgust", "embarrassment", "excitement", "fear", "gratitude", "grief", + "joy", "love", "nervousness", "optimism", "pride", "realization", "relief", + "remorse", "sadness", "surprise", "neutral" +] + +# ----------------------------- +# Load model and tokenizer +# ----------------------------- +MODEL_PATH = "/mount/src/emotion-classifier-nlp/outputs/model" +model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH, device_map="auto", low_cpu_mem_usage=True) +tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH) +print("Model device:", next(model.parameters()).device) + +# ----------------------------- +# Streamlit UI +# ----------------------------- +st.title("🧠 Emotion Classifier (NLP)") +st.markdown("Enter a sentence to analyze:") + +input_text = st.text_area(" ", height=100) + +if st.button("Classify") and input_text.strip(): + + with torch.no_grad(): + inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True) + outputs = model(**inputs) + probs = torch.nn.functional.softmax(outputs.logits, dim=1)[0] + + pred_label_idx = torch.argmax(probs).cpu().item() + pred_score = probs[pred_label_idx].cpu().item() + pred_emotion = GOEMOTIONS_LABELS[pred_label_idx] + + # ----------------------------- + # Confidence threshold logic + # ----------------------------- + threshold = 0.6 + if pred_score < threshold: + st.warning(f"**Predicted Emotion:** Unclear / Not enough signal (Confidence: {pred_score:.0%})") + else: + st.success(f"**Predicted Emotion:** {pred_emotion} (Confidence: {pred_score:.0%})") + + # ----------------------------- + # Show all probabilities + # ----------------------------- + st.markdown("### Prediction Probabilities:") + for i, prob in enumerate(probs): + st.write(f"- {GOEMOTIONS_LABELS[i]}: {prob.item():.4f}") + diff --git a/notebooks/.DS_Store b/notebooks/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..913a8fab09f72bc5a0743c83040c1481f0760f66 Binary files /dev/null and b/notebooks/.DS_Store differ diff --git a/notebooks/.ipynb_checkpoints/01_exploration-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/01_exploration-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..6126550fb1effc09a9f535893a1fb2cee6b33551 --- /dev/null +++ b/notebooks/.ipynb_checkpoints/01_exploration-checkpoint.ipynb @@ -0,0 +1,358 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "ec88103e-4da3-4eb9-9ed5-8aab3f7745a5", + "metadata": {}, + "outputs": [], + "source": [ + "from datasets import load_dataset\n", + "\n", + "dataset = load_dataset(\"go_emotions\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9e38e4c1-5c32-4e1b-8612-e723645e34bb", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from collections import Counter" + ] + }, + { + "cell_type": "markdown", + "id": "169cb7bc-8ac6-41d4-a109-c30ca1688899", + "metadata": {}, + "source": [ + "### Converting the dataset to a dataframe for ease" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "fadfe4cd-e0a3-4f09-a411-56db306fbd0b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0My favourite food is anything I didn't have to...[27]eebbqej
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3To make her feel threatened[14]ed7ypvh
4Dirty Southern Wankers[3]ed0bdzj
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" + ], + "text/plain": [ + " text labels id\n", + "0 My favourite food is anything I didn't have to... [27] eebbqej\n", + "1 Now if he does off himself, everyone will thin... [27] ed00q6i\n", + "2 WHY THE FUCK IS BAYLESS ISOING [2] eezlygj\n", + "3 To make her feel threatened [14] ed7ypvh\n", + "4 Dirty Southern Wankers [3] ed0bdzj" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame(dataset[\"train\"])\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "20a1d0af-1cd6-45c9-8128-29be160713d4", + "metadata": {}, + "outputs": [], + "source": [ + "label_counts = Counter() # counts number of occurences\n", + "\n", + "# Looping through all label lists and counting each label's total occurrences\n", + "for labels in df[\"labels\"]:\n", + " label_counts.update(labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "11c20083-c1a7-412d-9f98-e51defaded84", + "metadata": {}, + "outputs": [], + "source": [ + "# Mapping indices to label names\n", + "label_names = dataset[\"train\"].features[\"labels\"].feature.names\n", + "label_distribution = {label_names[i]: count for i, count in label_counts.items()}" + ] + }, + { + "cell_type": "markdown", + "id": "967e6d78-3dec-4dc5-b035-8ff263e689c8", + "metadata": {}, + "source": [ + "### Plot for the number of occurences of each emotion in the training set" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "91e04be4-4132-4c78-8e4e-6d4c1b29fd6c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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textlabelsidclean_text
0My favourite food is anything I didn't have to...[27]eebbqejmy favourite food is anything i didnt have to ...
1Now if he does off himself, everyone will thin...[27]ed00q6inow if he does off himself everyone will think...
2WHY THE FUCK IS BAYLESS ISOING[2]eezlygjwhy the fuck is bayless isoing
3To make her feel threatened[14]ed7ypvhto make her feel threatened
4Dirty Southern Wankers[3]ed0bdzjdirty southern wankers
5OmG pEyToN iSn'T gOoD eNoUgH tO hElP uS iN tHe...[26]edvnz26omg peyton isnt good enough to help us in the ...
6Yes I heard abt the f bombs! That has to be wh...[15]ee3b6wuyes i heard abt the f bombs that has to be why...
7We need more boards and to create a bit more s...[8, 20]ef4qmodwe need more boards and to create a bit more s...
8Damn youtube and outrage drama is super lucrat...[0]ed8wbdndamn youtube and outrage drama is super lucrat...
9It might be linked to the trust factor of your...[27]eczgv1oit might be linked to the trust factor of your...
\n", + "
" + ], + "text/plain": [ + " text labels id \\\n", + "0 My favourite food is anything I didn't have to... [27] eebbqej \n", + "1 Now if he does off himself, everyone will thin... [27] ed00q6i \n", + "2 WHY THE FUCK IS BAYLESS ISOING [2] eezlygj \n", + "3 To make her feel threatened [14] ed7ypvh \n", + "4 Dirty Southern Wankers [3] ed0bdzj \n", + "5 OmG pEyToN iSn'T gOoD eNoUgH tO hElP uS iN tHe... [26] edvnz26 \n", + "6 Yes I heard abt the f bombs! That has to be wh... [15] ee3b6wu \n", + "7 We need more boards and to create a bit more s... [8, 20] ef4qmod \n", + "8 Damn youtube and outrage drama is super lucrat... [0] ed8wbdn \n", + "9 It might be linked to the trust factor of your... [27] eczgv1o \n", + "\n", + " clean_text \n", + "0 my favourite food is anything i didnt have to ... \n", + "1 now if he does off himself everyone will think... \n", + "2 why the fuck is bayless isoing \n", + "3 to make her feel threatened \n", + "4 dirty southern wankers \n", + "5 omg peyton isnt good enough to help us in the ... \n", + "6 yes i heard abt the f bombs that has to be why... \n", + "7 we need more boards and to create a bit more s... \n", + "8 damn youtube and outrage drama is super lucrat... \n", + "9 it might be linked to the trust factor of your... " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import re\n", + "\n", + "def clean_text(text):\n", + " text = text.lower() # this makes every character in the text lower-cased\n", + " text = re.sub(r\"http\\S+|www\\S+|https\\S+\", '', text) # this removes links\n", + " text = re.sub(r'[^A-Za-z0-9\\s]+', '', text) # this removes special characters\n", + " text = re.sub(r'\\s+', ' ', text).strip() # this normalizes whitespace\n", + " return text\n", + "\n", + "df[\"clean_text\"] = df[\"text\"].apply(clean_text)\n", + "df.head(10)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/.ipynb_checkpoints/02_training-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/02_training-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..1766fe94d4c37d4b74a1e37bc496b0e5a53bb391 --- /dev/null +++ b/notebooks/.ipynb_checkpoints/02_training-checkpoint.ipynb @@ -0,0 +1,368 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "80f816c1-0839-41cb-847b-c79a62ca1465", + "metadata": {}, + "source": [ + "### Load all required modules for loading data, model setup, training, and metric evaluation" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "2554d05b-f08a-4c21-953f-4f507407e426", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import os\n", + "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), \"..\", \"src\")))\n", + "from data_loader import load_and_prepare_data \n", + "from model import get_model, get_tokenizer \n", + "from train import get_training_args, train_model \n", + "from evaluate import compute_metrics \n", + "from torch.utils.data import Dataset \n", + "import torch" + ] + }, + { + "cell_type": "markdown", + "id": "3bfbb706-4b0b-43de-a95a-884d46343668", + "metadata": {}, + "source": [ + "### Define a class that wraps tokenized data and labels for Hugging Face’s Trainer to use" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c814c354-7962-4a2d-b7bd-5c498f1d004e", + "metadata": {}, + "outputs": [], + "source": [ + "class EmotionDataset(Dataset):\n", + " def __init__(self, encodings, labels):\n", + " self.encodings = encodings # BERT tokenized inputs (input_ids, attention_mask)\n", + " self.labels = labels # Encoded labels (integers)\n", + "\n", + " def __len__(self):\n", + " return len(self.labels) # Total number of samples\n", + "\n", + " def __getitem__(self, idx):\n", + " # Return dictionary of input tensors + label tensor for a single sample\n", + " return {\n", + " key: torch.tensor(val[idx]) for key, val in self.encodings.items()\n", + " } | {\"labels\": torch.tensor(self.labels[idx])}" + ] + }, + { + "cell_type": "markdown", + "id": "f9b87257-f0c0-4532-9eee-939d8747ef79", + "metadata": {}, + "source": [ + "### Load the dataset from Hugging Face, clean and encode it, then tokenize it using the BERT tokenizer." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "18e312be-5863-4e24-900a-843e42e145cc", + "metadata": {}, + "outputs": [], + "source": [ + "# Load train/test splits and label encoder\n", + "train_texts, test_texts, train_labels, test_labels, label_encoder = load_and_prepare_data()\n", + "\n", + "# Load BERT tokenizer\n", + "tokenizer = get_tokenizer()\n", + "\n", + "# Tokenize training and testing texts with truncation and padding\n", + "train_encodings = tokenizer(train_texts, truncation=True, padding=True, max_length=128)\n", + "test_encodings = tokenizer(test_texts, truncation=True, padding=True, max_length=128)\n", + "\n", + "# Wrap the tokenized data into EmotionDataset objects\n", + "train_dataset = EmotionDataset(train_encodings, train_labels)\n", + "test_dataset = EmotionDataset(test_encodings, test_labels)" + ] + }, + { + "cell_type": "markdown", + "id": "66b99b4e-5297-4bc0-8cfb-20dbe22526c0", + "metadata": {}, + "source": [ + "### Samples from the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "35db4426-db21-4438-ba0e-ebb51d52edfb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sample 1\n", + "Text: i'd just feel less out of place, i guess. my sa makes me feel like i'm so behind my peers in terms of a social life\n", + "Label (encoded): 9\n", + "\n", + "Sample 2\n", + "Text: i love the lady in the green jacket chasing after the second car looking back at the first car like \"look what you did\"\n", + "Label (encoded): 18\n", + "\n", + "Sample 3\n", + "Text: man. really bad last possession there. bummer.\n", + "Label (encoded): 10\n", + "\n", + "Sample 4\n", + "Text: never would’ve guessed that one.\n", + "Label (encoded): 20\n", + "\n", + "Sample 5\n", + "Text: i wasn’t even expecting the reply that’s why i’m literally bamboozled.\n", + "Label (encoded): 27\n", + "\n" + ] + } + ], + "source": [ + "for i in range(5):\n", + " print(f\"Sample {i+1}\")\n", + " print(f\"Text: {train_texts[i]}\")\n", + " print(f\"Label (encoded): {train_labels[i]}\")\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "id": "0883760a-a449-42ca-ba69-fa01d874e50b", + "metadata": {}, + "source": [ + "### Set up the BERT model for sequence classification and define training parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "3176ccf4-d20d-460c-b620-c73a1ab9cb6d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n", + "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n", + "/opt/anaconda3/lib/python3.12/site-packages/transformers/training_args.py:1545: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "# Load pre-trained BERT model with classification head for number of emotion classes\n", + "model = get_model(num_labels=len(label_encoder.classes_))\n", + "\n", + "# Set training configuration: batch size, epochs, logging, saving, evaluation\n", + "training_args = get_training_args()" + ] + }, + { + "cell_type": "markdown", + "id": "874a4e6a-80dd-470d-9283-e1c88e731b8e", + "metadata": {}, + "source": [ + "### Train the Model " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4c312e56-52bf-417d-82c0-8a1f47b82670", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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EpochTraining LossValidation LossAccuracyF1
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EpochTraining LossValidation LossAccuracyF1
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" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "TrainOutput(global_step=5448, training_loss=0.7054264770818002, metrics={'train_runtime': 5721.3012, 'train_samples_per_second': 15.23, 'train_steps_per_second': 0.952, 'total_flos': 5733080823638016.0, 'train_loss': 0.7054264770818002, 'epoch': 3.0})" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trainer = train_model(\n", + " model=model,\n", + " args=training_args,\n", + " train_dataset=train_dataset,\n", + " val_dataset=test_dataset,\n", + " compute_metrics=compute_metrics\n", + ")\n", + "\n", + "# Begin training\n", + "trainer.train()" + ] + }, + { + "cell_type": "markdown", + "id": "020729b6-c545-42ba-bd2c-00ee5f9bbb80", + "metadata": {}, + "source": [ + "### Save both model weights and tokenizer files for future inference or deployment." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "5f12aedb-b3f8-4a1b-8e1f-6a68eb29933f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('../outputs/model/tokenizer_config.json',\n", + " '../outputs/model/special_tokens_map.json',\n", + " '../outputs/model/vocab.txt',\n", + " '../outputs/model/added_tokens.json')" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pathlib import Path\n", + "model_path = Path(\"..\") / \"outputs\" / \"model\"\n", + "model.save_pretrained(model_path)\n", + "tokenizer.save_pretrained(model_path)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/.ipynb_checkpoints/03_evaluation-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/03_evaluation-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..e5bb9e7a797df1e80409c96ffcfac08369514979 --- /dev/null +++ b/notebooks/.ipynb_checkpoints/03_evaluation-checkpoint.ipynb @@ -0,0 +1,716 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "5df279f6-36ad-4b03-b31e-3f29467f466c", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import os\n", + "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), \"..\", \"src\")))\n", + "from data_loader import load_and_prepare_data\n", + "from evaluate import save_confusion_matrix, save_metrics\n", + "from model import get_tokenizer\n", + "from transformers import BertForSequenceClassification\n", + "import torch\n", + "import numpy as np\n", + "from pathlib import Path\n", + "from transformers import Trainer\n", + "from captum.attr import IntegratedGradients, LayerIntegratedGradients, visualization\n", + "from transformers import BertTokenizer, BertForSequenceClassification\n", + "import random\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as mpimg\n", + "from IPython.display import display, Image, HTML" + ] + }, + { + "cell_type": "markdown", + "id": "a2994399-f54d-4d01-85fd-7437055d53d2", + "metadata": {}, + "source": [ + "### Reload Saved Model and Tokenizer" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c3fd3a1e-57fd-4450-abb7-ce547f2544b1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "BertForSequenceClassification(\n", + " (bert): BertModel(\n", + " (embeddings): BertEmbeddings(\n", + " (word_embeddings): Embedding(30522, 768, padding_idx=0)\n", + " (position_embeddings): Embedding(512, 768)\n", + " (token_type_embeddings): Embedding(2, 768)\n", + " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " (encoder): BertEncoder(\n", + " (layer): ModuleList(\n", + " (0-11): 12 x BertLayer(\n", + " (attention): BertAttention(\n", + " (self): BertSdpaSelfAttention(\n", + " (query): Linear(in_features=768, out_features=768, bias=True)\n", + " (key): Linear(in_features=768, out_features=768, bias=True)\n", + " (value): Linear(in_features=768, out_features=768, bias=True)\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " (output): BertSelfOutput(\n", + " (dense): Linear(in_features=768, out_features=768, bias=True)\n", + " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " )\n", + " (intermediate): BertIntermediate(\n", + " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", + " (intermediate_act_fn): GELUActivation()\n", + " )\n", + " (output): BertOutput(\n", + " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", + " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " )\n", + " (pooler): BertPooler(\n", + " (dense): Linear(in_features=768, out_features=768, bias=True)\n", + " (activation): Tanh()\n", + " )\n", + " )\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " (classifier): Linear(in_features=768, out_features=28, bias=True)\n", + ")" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_path = Path(\"..\") / \"outputs\" / \"model\"\n", + "\n", + "# Load trained model and tokenizer\n", + "model = BertForSequenceClassification.from_pretrained(model_path)\n", + "tokenizer = get_tokenizer().from_pretrained(model_path)\n", + "model.eval()" + ] + }, + { + "cell_type": "markdown", + "id": "eed752f5-f464-4546-9f96-84ab3487efd4", + "metadata": {}, + "source": [ + "### Prepare Validation Data for Evaluation" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "29f9d9d9-1c5f-49e0-b0b1-e48150926256", + "metadata": {}, + "outputs": [], + "source": [ + "# Reload test split and labels using same preprocessing logic as training\n", + "train_texts, test_texts, train_labels, test_labels, label_encoder = load_and_prepare_data()\n", + "\n", + "# Tokenize test texts\n", + "test_encodings = tokenizer(test_texts, truncation=True, padding=True, max_length=128)\n", + "\n", + "# Wrap validation data in custom dataset class\n", + "from torch.utils.data import Dataset\n", + "\n", + "class EmotionDataset(Dataset):\n", + " def __init__(self, encodings, labels):\n", + " self.encodings = encodings\n", + " self.labels = labels\n", + "\n", + " def __len__(self):\n", + " return len(self.labels)\n", + "\n", + " def __getitem__(self, idx):\n", + " return {\n", + " key: torch.tensor(val[idx]) for key, val in self.encodings.items()\n", + " } | {\"labels\": torch.tensor(self.labels[idx])}\n", + "\n", + "test_dataset = EmotionDataset(test_encodings, test_labels)" + ] + }, + { + "cell_type": "markdown", + "id": "fc1a891f-8742-4e35-a16b-a0dd14f58f51", + "metadata": {}, + "source": [ + "### Generate Predictions using the Hugging Face Trainer" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b84c7173-89c5-43fd-9abf-88434d987d08", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Initialize Hugging Face Trainer with the loaded model\n", + "trainer = Trainer(model=model)\n", + "\n", + "# Generate raw predictions\n", + "predictions = trainer.predict(test_dataset)\n", + "\n", + "# Extract labels and predicted classes\n", + "y_true = predictions.label_ids\n", + "y_pred = np.argmax(predictions.predictions, axis=1)" + ] + }, + { + "cell_type": "markdown", + "id": "521ddf99-70e4-4a41-8df8-3674118f7afe", + "metadata": {}, + "source": [ + "### Generate the Classification Report" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "64fed2bb-e5d3-4d9a-8c02-6a6ceb2c748c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Classification Report:\n", + "\n", + " precision recall f1-score support\n", + "\n", + " admiration 0.67 0.76 0.71 542\n", + " amusement 0.73 0.85 0.78 330\n", + " anger 0.43 0.55 0.48 205\n", + " annoyance 0.30 0.19 0.23 290\n", + " approval 0.38 0.37 0.38 375\n", + " caring 0.38 0.48 0.42 130\n", + " confusion 0.50 0.42 0.46 172\n", + " curiosity 0.46 0.60 0.52 278\n", + " desire 0.64 0.53 0.58 78\n", + "disappointment 0.33 0.20 0.25 142\n", + " disapproval 0.32 0.48 0.39 280\n", + " disgust 0.51 0.45 0.48 100\n", + " embarrassment 0.79 0.56 0.66 41\n", + " excitement 0.42 0.31 0.36 102\n", + " fear 0.81 0.50 0.62 86\n", + " gratitude 0.91 0.88 0.90 371\n", + " grief 0.00 0.00 0.00 8\n", + " joy 0.55 0.58 0.56 171\n", + " love 0.78 0.85 0.81 285\n", + " nervousness 0.46 0.35 0.40 17\n", + " neutral 0.68 0.66 0.67 2565\n", + " optimism 0.67 0.56 0.61 172\n", + " pride 0.33 0.10 0.15 10\n", + " realization 0.56 0.15 0.24 117\n", + " relief 0.00 0.00 0.00 17\n", + " remorse 0.61 0.85 0.71 71\n", + " sadness 0.55 0.50 0.53 163\n", + " surprise 0.57 0.66 0.61 144\n", + "\n", + " accuracy 0.60 7262\n", + " macro avg 0.51 0.48 0.48 7262\n", + " weighted avg 0.60 0.60 0.60 7262\n", + "\n" + ] + } + ], + "source": [ + "from sklearn.metrics import classification_report\n", + "\n", + "# Create and print the classification report\n", + "report = classification_report(\n", + " y_true, \n", + " y_pred, \n", + " target_names=label_encoder.classes_,\n", + " zero_division=0\n", + ")\n", + "print(\"Classification Report:\\n\")\n", + "print(report)\n", + "\n", + "# Also save it as JSON\n", + "from sklearn.metrics import classification_report\n", + "import json\n", + "from pathlib import Path\n", + "\n", + "metrics_dir = Path(\"..\") / \"outputs\" / \"metrics\"\n", + "metrics_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + "# Save the report in dictionary form for precision/recall/F1\n", + "report_dict = classification_report(\n", + " y_true, \n", + " y_pred, \n", + " target_names=label_encoder.classes_, \n", + " output_dict=True,\n", + " zero_division=0)\n", + "\n", + "with open(metrics_dir / \"report.json\", \"w\") as f:\n", + " json.dump(report_dict, f, indent=4)" + ] + }, + { + "cell_type": "markdown", + "id": "e9d1b6e7-08cf-424d-8a01-afc59f5f7cea", + "metadata": {}, + "source": [ + "### Plot the Confusion Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "a7ed7ed2-c295-4819-8cf6-e0ea4b0efbfa", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Compute confusion matrix\n", + "cm = confusion_matrix(y_true, y_pred)\n", + "\n", + "# Plot it\n", + "plt.figure(figsize=(12, 8))\n", + "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=label_encoder.classes_, yticklabels=label_encoder.classes_)\n", + "plt.xlabel(\"Predicted\")\n", + "plt.ylabel(\"True\")\n", + "plt.title(\"Confusion Matrix\")\n", + "plt.xticks(rotation=45)\n", + "plt.yticks(rotation=0)\n", + "plt.tight_layout()\n", + "plt.savefig(metrics_dir / \"confusion_matrix.png\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "88f43efd-2b75-4d13-86dd-ad7f52cc3fb2", + "metadata": {}, + "source": [ + "## Interpretability with Captum\n", + "\n", + "In this section, we apply the Integrated Gradients method from Captum to better understand how the model makes its predictions. \n", + "We randomly select 5 validation samples, visualize token importance, and analyze how different words contribute to the model’s decision-making." + ] + }, + { + "cell_type": "markdown", + "id": "d856417e-03a6-4b26-98a1-1ab11c07695e", + "metadata": {}, + "source": [ + "### Interpretation Summary\n", + "\n", + "The above visualizations highlight which words in each text sample had the most influence on the model’s prediction. \n", + "Larger importance values (darker colors or taller bars) indicate stronger contribution to the decision. \n", + "This helps us verify whether the model is focusing on relevant keywords when classifying emotions." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e8d55198-c708-4659-93b9-30f2ca3a098e", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the custom forward function\n", + "def forward_func(inputs_embeds, attention_mask):\n", + " outputs = model(inputs_embeds=inputs_embeds, attention_mask=attention_mask)\n", + " return torch.softmax(outputs.logits, dim=1)\n", + "\n", + "# Initialize Integrated Gradients\n", + "ig = IntegratedGradients(forward_func)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "2935c1d5-9811-4825-8f68-8594d7728f97", + "metadata": {}, + "outputs": [], + "source": [ + "# Pick 5 random samples\n", + "sample_indices = random.sample(range(len(test_texts)), 5)\n", + "sample_texts = [test_texts[i] for i in sample_indices]\n", + "sample_labels = [test_labels[i] for i in sample_indices]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "fd160f66-9c1f-4859-884b-1536a7da498f", + "metadata": {}, + "outputs": [], + "source": [ + "# Define helper functions for plotting\n", + "def plot_heatmap(tokens, attributions, label_name, idx):\n", + " \"\"\"\n", + " Plots and saves a heatmap of token attributions.\n", + " \"\"\"\n", + " attributions_np = attributions.detach().cpu().numpy()\n", + " fig, ax = plt.subplots(figsize=(len(tokens) * 0.6, 2))\n", + " ax.bar(range(len(tokens)), attributions_np, color='lightblue')\n", + " ax.set_xticks(range(len(tokens)))\n", + " ax.set_xticklabels(tokens, rotation=45, ha=\"right\", fontsize=8)\n", + " ax.set_title(f\"Sample {idx+1} - True Label: {label_name}\", fontsize=10)\n", + " plt.tight_layout()\n", + " plt.savefig(f\"../outputs/interpretations/sample_{idx+1}_{label_name}_heatmap.png\")\n", + " plt.show()\n", + "\n", + "def plot_bar(tokens, attributions, label_name, idx):\n", + " \"\"\"\n", + " Plots and saves a horizontal bar chart of token attributions.\n", + " \"\"\"\n", + " attributions_np = attributions.detach().cpu().numpy()\n", + " tokens_np = np.array(tokens)\n", + " fig, ax = plt.subplots(figsize=(6, len(tokens) * 0.4))\n", + " ax.barh(tokens_np, attributions_np, color='salmon')\n", + " ax.set_title(f\"Attribution Scores (Bar) - Sample {idx+1}: {label_name}\", fontsize=10)\n", + " ax.invert_yaxis()\n", + " plt.tight_layout()\n", + " plt.savefig(f\"../outputs/interpretations/sample_{idx+1}_{label_name}_bar.png\")\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "c6b7ee4b-67d6-4ca4-9bae-2ea35c5af9d9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "BertForSequenceClassification(\n", + " (bert): BertModel(\n", + " (embeddings): BertEmbeddings(\n", + " (word_embeddings): Embedding(30522, 768, padding_idx=0)\n", + " (position_embeddings): Embedding(512, 768)\n", + " (token_type_embeddings): Embedding(2, 768)\n", + " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " (encoder): BertEncoder(\n", + " (layer): ModuleList(\n", + " (0-11): 12 x BertLayer(\n", + " (attention): BertAttention(\n", + " (self): BertSdpaSelfAttention(\n", + " (query): Linear(in_features=768, out_features=768, bias=True)\n", + " (key): Linear(in_features=768, out_features=768, bias=True)\n", + " (value): Linear(in_features=768, out_features=768, bias=True)\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " (output): BertSelfOutput(\n", + " (dense): Linear(in_features=768, out_features=768, bias=True)\n", + " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " )\n", + " (intermediate): BertIntermediate(\n", + " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", + " (intermediate_act_fn): GELUActivation()\n", + " )\n", + " (output): BertOutput(\n", + " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", + " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " )\n", + " (pooler): BertPooler(\n", + " (dense): Linear(in_features=768, out_features=768, bias=True)\n", + " (activation): Tanh()\n", + " )\n", + " )\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " (classifier): Linear(in_features=768, out_features=28, bias=True)\n", + ")" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "device = torch.device(\"cpu\")\n", + "model.to(device)\n", + "model.eval()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "76359edf-453f-4afe-9b74-ea5bcb5d3066", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved interpretation plots for sample 1: 'disapproval'\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved interpretation plots for sample 2: 'neutral'\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved interpretation plots for sample 3: 'neutral'\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved interpretation plots for sample 4: 'sadness'\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved interpretation plots for sample 5: 'neutral'\n" + ] + } + ], + "source": [ + "for idx, (text, label_idx) in enumerate(zip(sample_texts, sample_labels)):\n", + " # Encode input\n", + " encoded = tokenizer(text, return_tensors='pt', truncation=True, padding=True, max_length=128)\n", + " input_ids = encoded['input_ids'].cpu().long()\n", + " attention_mask = encoded['attention_mask'].cpu().long()\n", + "\n", + " # Get embeddings (input for IG)\n", + " inputs_embeds = model.get_input_embeddings()(input_ids)\n", + "\n", + " # Compute attributions\n", + " attributions, delta = ig.attribute(\n", + " inputs=inputs_embeds,\n", + " additional_forward_args=attention_mask,\n", + " target=label_idx,\n", + " return_convergence_delta=True\n", + " )\n", + "\n", + " # Convert input_ids to tokens\n", + " tokens = tokenizer.convert_ids_to_tokens(input_ids.squeeze())\n", + "\n", + " # Mean across embedding dimension\n", + " attributions_sum = attributions.sum(dim=-1).squeeze(0)\n", + " attributions_sum = attributions_sum / torch.norm(attributions_sum)\n", + "\n", + " # Map label index to name\n", + " label_name = label_encoder.inverse_transform([label_idx])[0]\n", + "\n", + " # Plot\n", + " plot_heatmap(tokens, attributions_sum, label_name, idx)\n", + " plot_bar(tokens, attributions_sum, label_name, idx)\n", + "\n", + " print(f\"Saved interpretation plots for sample {idx+1}: '{label_name}'\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "e6a06489-1aba-4f4a-a212-7ee5db8bf22c", + "metadata": {}, + "source": [ + "### Interpretation of Integrated Gradients Attributions" + ] + }, + { + "cell_type": "markdown", + "id": "340fb1bb-8882-4c60-aa4c-4ca4af85fc2f", + "metadata": {}, + "source": [ + "The Integrated Gradients plots (both heatmaps and bar charts) reveal how individual tokens contribute to the model’s classification of each emotion label. Below is an analysis of all five random samples:\n", + "\n", + "Sample 1 – Disapproval\n", + "The tokens **##thic** and **##al** (from the word “unethical”) show the highest positive attribution toward the “disapproval” label. This indicates the model effectively identifies morally charged terms as central to the emotion. Tokens like “corporations” and “universally” also contribute, though to a lesser degree, suggesting the model picks up on critique of systemic entities.\n", + "\n", + "Sample 2 – Neutral\n", + "Interestingly, the word “luckily” has a strong negative attribution, meaning it detracted from a more emotional label being assigned. Despite its connotation, the surrounding context likely flattened its impact. This reflects how nuanced sentiment-neutral language can shift overall classification.\n", + "\n", + "Sample 3 – Neutral\n", + "The word “do” and the **[SEP]** token dominate the attribution, implying that minimal semantic content influenced the prediction. The model appears to correctly assign a neutral label to short, affirmative phrases like “yes definitely do,” focusing more on structure than emotional tone.\n", + "\n", + "Sample 4 – Sadness\n", + "As expected, the word “sad” has the highest attribution, confirming the model’s reliance on explicit emotional language. Subword tokens like **##des** and “**##t**” also contribute meaningfully, which shows the tokenizer’s granularity doesn’t hinder attribution clarity. This attribution pattern demonstrates successful emotion recognition when keywords are present.\n", + "\n", + "Sample 5 – Neutral\n", + "This example includes emotionally suggestive words like “dying” and “dad,” but the overall context lacks sufficient emotional framing. The model attributes some weight to punctuation and the **[SEP]** token, suggesting it interprets the message as incomplete or vague—supporting a neutral label.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "951ffb3a-3d7c-4e94-b103-c25ae23e7973", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/.ipynb_checkpoints/04_model_comparison-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/04_model_comparison-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..952481a97d8981528c1a43ff891307faae7f47ec --- /dev/null +++ b/notebooks/.ipynb_checkpoints/04_model_comparison-checkpoint.ipynb @@ -0,0 +1,290 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "4032a920-2db8-4977-8b4f-a5a771dd022f", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import os\n", + "\n", + "project_root = os.path.abspath(os.path.join(os.getcwd(), \"..\"))\n", + "sys.path.append(project_root)\n", + "\n", + "from transformers import pipeline\n", + "from src.model_hartmann import load_model as load_hartmann_model, load_tokenizer as load_hartmann_tokenizer\n", + "from src.model_custom import load_model as load_custom_model, load_tokenizer as load_custom_tokenizer" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "525cf57e-4ec3-40fd-aca2-0e9700a73298", + "metadata": {}, + "outputs": [], + "source": [ + "hartmann_model = load_hartmann_model()\n", + "hartmann_tokenizer = load_hartmann_tokenizer()\n", + "\n", + "custom_model = load_custom_model()\n", + "custom_tokenizer = load_custom_tokenizer()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "04f9415c-3d4f-4ac0-8f51-74ec4bd64293", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Hardware accelerator e.g. GPU is available in the environment, but no `device` argument is passed to the `Pipeline` object. Model will be on CPU.\n", + "/opt/anaconda3/lib/python3.12/site-packages/transformers/pipelines/text_classification.py:104: UserWarning: `return_all_scores` is now deprecated, if want a similar functionality use `top_k=None` instead of `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.\n", + " warnings.warn(\n", + "Hardware accelerator e.g. GPU is available in the environment, but no `device` argument is passed to the `Pipeline` object. Model will be on CPU.\n" + ] + } + ], + "source": [ + "# Create pipelines for easy predictions\n", + "hartmann_pipeline = pipeline(\"text-classification\", model=hartmann_model, tokenizer=hartmann_tokenizer, return_all_scores=True)\n", + "custom_pipeline = pipeline(\"text-classification\", model=custom_model, tokenizer=custom_tokenizer, return_all_scores=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3fcdf650-3abc-42a6-b1fd-0129e49d1e68", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "========= Sentence 1 ==========\n", + "Text: I love spending time with my family.\n", + "\n", + "--- Hartmann Model Top 3 Predictions ---\n", + "joy: 0.9883\n", + "sadness: 0.0067\n", + "disgust: 0.0013\n", + "\n", + "--- Pretrained Model Top 3 Predictions ---\n", + "love: 0.9536\n", + "joy: 0.0134\n", + "admiration: 0.0108\n", + "\n", + "\n", + "========= Sentence 2 ==========\n", + "Text: This is the worst day of my life.\n", + "\n", + "--- Hartmann Model Top 3 Predictions ---\n", + "disgust: 0.9805\n", + "anger: 0.0086\n", + "sadness: 0.0055\n", + "\n", + "--- Pretrained Model Top 3 Predictions ---\n", + "anger: 0.3353\n", + "surprise: 0.2010\n", + "disgust: 0.1235\n", + "\n", + "\n", + "========= Sentence 3 ==========\n", + "Text: I'm feeling very nervous about the exam.\n", + "\n", + "--- Hartmann Model Top 3 Predictions ---\n", + "fear: 0.9947\n", + "sadness: 0.0013\n", + "joy: 0.0011\n", + "\n", + "--- Pretrained Model Top 3 Predictions ---\n", + "nervousness: 0.6201\n", + "fear: 0.0828\n", + "embarrassment: 0.0393\n", + "\n", + "\n", + "========= Sentence 4 ==========\n", + "Text: What a beautiful sunset!\n", + "\n", + "--- Hartmann Model Top 3 Predictions ---\n", + "joy: 0.8377\n", + "surprise: 0.1189\n", + "neutral: 0.0221\n", + "\n", + "--- Pretrained Model Top 3 Predictions ---\n", + "admiration: 0.8548\n", + "excitement: 0.0729\n", + "joy: 0.0351\n", + "\n", + "\n", + "========= Sentence 5 ==========\n", + "Text: I feel so disappointed and frustrated with the situation.\n", + "\n", + "--- Hartmann Model Top 3 Predictions ---\n", + "sadness: 0.9310\n", + "anger: 0.0381\n", + "disgust: 0.0158\n", + "\n", + "--- Pretrained Model Top 3 Predictions ---\n", + "disappointment: 0.5645\n", + "annoyance: 0.1864\n", + "anger: 0.0736\n", + "\n", + "\n", + "========= Sentence 6 ==========\n", + "Text: I'm not sure how to feel about this.\n", + "\n", + "--- Hartmann Model Top 3 Predictions ---\n", + "neutral: 0.5698\n", + "disgust: 0.2213\n", + "sadness: 0.0720\n", + "\n", + "--- Pretrained Model Top 3 Predictions ---\n", + "confusion: 0.9011\n", + "optimism: 0.0230\n", + "disapproval: 0.0223\n", + "\n", + "\n", + "========= Sentence 7 ==========\n", + "Text: That was hilarious, I can't stop laughing!\n", + "\n", + "--- Hartmann Model Top 3 Predictions ---\n", + "joy: 0.9336\n", + "surprise: 0.0306\n", + "neutral: 0.0178\n", + "\n", + "--- Pretrained Model Top 3 Predictions ---\n", + "amusement: 0.9551\n", + "joy: 0.0286\n", + "optimism: 0.0032\n", + "\n", + "\n", + "========= Sentence 8 ==========\n", + "Text: I feel completely empty and lost.\n", + "\n", + "--- Hartmann Model Top 3 Predictions ---\n", + "sadness: 0.9808\n", + "neutral: 0.0086\n", + "disgust: 0.0051\n", + "\n", + "--- Pretrained Model Top 3 Predictions ---\n", + "surprise: 0.8055\n", + "disappointment: 0.1067\n", + "optimism: 0.0222\n", + "\n", + "\n", + "========= Sentence 9 ==========\n", + "Text: Your help means a lot to me, thank you!\n", + "\n", + "--- Hartmann Model Top 3 Predictions ---\n", + "joy: 0.9760\n", + "neutral: 0.0104\n", + "surprise: 0.0057\n", + "\n", + "--- Pretrained Model Top 3 Predictions ---\n", + "gratitude: 0.9890\n", + "caring: 0.0014\n", + "sadness: 0.0009\n", + "\n", + "\n", + "========= Sentence 10 ==========\n", + "Text: I'm so angry I could scream.\n", + "\n", + "--- Hartmann Model Top 3 Predictions ---\n", + "anger: 0.9785\n", + "fear: 0.0084\n", + "neutral: 0.0047\n", + "\n", + "--- Pretrained Model Top 3 Predictions ---\n", + "anger: 0.9155\n", + "annoyance: 0.0223\n", + "optimism: 0.0082\n", + "\n", + "\n" + ] + } + ], + "source": [ + "from tabulate import tabulate\n", + "\n", + "goemotions_labels = [\n", + " \"admiration\", \"amusement\", \"anger\", \"annoyance\", \"approval\", \"caring\", \"confusion\", \"curiosity\",\n", + " \"desire\", \"disappointment\", \"disapproval\", \"disgust\", \"embarrassment\", \"excitement\", \"fear\",\n", + " \"gratitude\", \"grief\", \"joy\", \"love\", \"nervousness\", \"optimism\", \"pride\", \"realization\", \"relief\",\n", + " \"remorse\", \"sadness\", \"surprise\", \"neutral\"\n", + "]\n", + "\n", + "\n", + "# Your 10 test sentences\n", + "sentences = [\n", + " \"I love spending time with my family.\",\n", + " \"This is the worst day of my life.\",\n", + " \"I'm feeling very nervous about the exam.\",\n", + " \"What a beautiful sunset!\",\n", + " \"I feel so disappointed and frustrated with the situation.\",\n", + " \"I'm not sure how to feel about this.\",\n", + " \"That was hilarious, I can't stop laughing!\",\n", + " \"I feel completely empty and lost.\",\n", + " \"Your help means a lot to me, thank you!\",\n", + " \"I'm so angry I could scream.\"\n", + "]\n", + "\n", + "# Loop over sentences and collect results\n", + "for i, sentence in enumerate(sentences):\n", + " print(f\"========= Sentence {i+1} ==========\")\n", + " print(f\"Text: {sentence}\\n\")\n", + "\n", + " # Get predictions\n", + " hartmann_results = hartmann_pipeline(sentence, return_all_scores=True)\n", + " custom_results = custom_pipeline(sentence, return_all_scores=True)\n", + "\n", + " # Unwrap the list to get the actual results\n", + " hartmann_results = hartmann_results[0]\n", + " custom_results = custom_results[0]\n", + "\n", + " # Sort and get top 3 predictions for each\n", + " hartmann_top3 = sorted(hartmann_results, key=lambda x: x['score'], reverse=True)[:3]\n", + " custom_top3 = sorted(custom_results, key=lambda x: x['score'], reverse=True)[:3]\n", + "\n", + " # Display Hartmann predictions\n", + " print(\"--- Hartmann Model Top 3 Predictions ---\")\n", + " for res in hartmann_top3:\n", + " print(f\"{res['label']}: {res['score']:.4f}\")\n", + "\n", + " # Display Custom Model predictions\n", + " print(\"\\n--- Pretrained Model Top 3 Predictions ---\")\n", + " for res in custom_top3:\n", + " label_idx = int(res['label'].split(\"_\")[-1])\n", + " emotion = goemotions_labels[label_idx]\n", + " print(f\"{emotion}: {res['score']:.4f}\")\n", + "\n", + " print(\"\\n\")\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/01_exploration.ipynb b/notebooks/01_exploration.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..6126550fb1effc09a9f535893a1fb2cee6b33551 --- /dev/null +++ b/notebooks/01_exploration.ipynb @@ -0,0 +1,358 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "ec88103e-4da3-4eb9-9ed5-8aab3f7745a5", + "metadata": {}, + "outputs": [], + "source": [ + "from datasets import load_dataset\n", + "\n", + "dataset = load_dataset(\"go_emotions\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9e38e4c1-5c32-4e1b-8612-e723645e34bb", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from collections import Counter" + ] + }, + { + "cell_type": "markdown", + "id": "169cb7bc-8ac6-41d4-a109-c30ca1688899", + "metadata": {}, + "source": [ + "### Converting the dataset to a dataframe for ease" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "fadfe4cd-e0a3-4f09-a411-56db306fbd0b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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textlabelsid
0My favourite food is anything I didn't have to...[27]eebbqej
1Now if he does off himself, everyone will thin...[27]ed00q6i
2WHY THE FUCK IS BAYLESS ISOING[2]eezlygj
3To make her feel threatened[14]ed7ypvh
4Dirty Southern Wankers[3]ed0bdzj
\n", + "
" + ], + "text/plain": [ + " text labels id\n", + "0 My favourite food is anything I didn't have to... [27] eebbqej\n", + "1 Now if he does off himself, everyone will thin... [27] ed00q6i\n", + "2 WHY THE FUCK IS BAYLESS ISOING [2] eezlygj\n", + "3 To make her feel threatened [14] ed7ypvh\n", + "4 Dirty Southern Wankers [3] ed0bdzj" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame(dataset[\"train\"])\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "20a1d0af-1cd6-45c9-8128-29be160713d4", + "metadata": {}, + "outputs": [], + "source": [ + "label_counts = Counter() # counts number of occurences\n", + "\n", + "# Looping through all label lists and counting each label's total occurrences\n", + "for labels in df[\"labels\"]:\n", + " label_counts.update(labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "11c20083-c1a7-412d-9f98-e51defaded84", + "metadata": {}, + "outputs": [], + "source": [ + "# Mapping indices to label names\n", + "label_names = dataset[\"train\"].features[\"labels\"].feature.names\n", + "label_distribution = {label_names[i]: count for i, count in label_counts.items()}" + ] + }, + { + "cell_type": "markdown", + "id": "967e6d78-3dec-4dc5-b035-8ff263e689c8", + "metadata": {}, + "source": [ + "### Plot for the number of occurences of each emotion in the training set" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "91e04be4-4132-4c78-8e4e-6d4c1b29fd6c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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textlabelsidclean_text
0My favourite food is anything I didn't have to...[27]eebbqejmy favourite food is anything i didnt have to ...
1Now if he does off himself, everyone will thin...[27]ed00q6inow if he does off himself everyone will think...
2WHY THE FUCK IS BAYLESS ISOING[2]eezlygjwhy the fuck is bayless isoing
3To make her feel threatened[14]ed7ypvhto make her feel threatened
4Dirty Southern Wankers[3]ed0bdzjdirty southern wankers
5OmG pEyToN iSn'T gOoD eNoUgH tO hElP uS iN tHe...[26]edvnz26omg peyton isnt good enough to help us in the ...
6Yes I heard abt the f bombs! That has to be wh...[15]ee3b6wuyes i heard abt the f bombs that has to be why...
7We need more boards and to create a bit more s...[8, 20]ef4qmodwe need more boards and to create a bit more s...
8Damn youtube and outrage drama is super lucrat...[0]ed8wbdndamn youtube and outrage drama is super lucrat...
9It might be linked to the trust factor of your...[27]eczgv1oit might be linked to the trust factor of your...
\n", + "
" + ], + "text/plain": [ + " text labels id \\\n", + "0 My favourite food is anything I didn't have to... [27] eebbqej \n", + "1 Now if he does off himself, everyone will thin... [27] ed00q6i \n", + "2 WHY THE FUCK IS BAYLESS ISOING [2] eezlygj \n", + "3 To make her feel threatened [14] ed7ypvh \n", + "4 Dirty Southern Wankers [3] ed0bdzj \n", + "5 OmG pEyToN iSn'T gOoD eNoUgH tO hElP uS iN tHe... [26] edvnz26 \n", + "6 Yes I heard abt the f bombs! That has to be wh... [15] ee3b6wu \n", + "7 We need more boards and to create a bit more s... [8, 20] ef4qmod \n", + "8 Damn youtube and outrage drama is super lucrat... [0] ed8wbdn \n", + "9 It might be linked to the trust factor of your... [27] eczgv1o \n", + "\n", + " clean_text \n", + "0 my favourite food is anything i didnt have to ... \n", + "1 now if he does off himself everyone will think... \n", + "2 why the fuck is bayless isoing \n", + "3 to make her feel threatened \n", + "4 dirty southern wankers \n", + "5 omg peyton isnt good enough to help us in the ... \n", + "6 yes i heard abt the f bombs that has to be why... \n", + "7 we need more boards and to create a bit more s... \n", + "8 damn youtube and outrage drama is super lucrat... \n", + "9 it might be linked to the trust factor of your... " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import re\n", + "\n", + "def clean_text(text):\n", + " text = text.lower() # this makes every character in the text lower-cased\n", + " text = re.sub(r\"http\\S+|www\\S+|https\\S+\", '', text) # this removes links\n", + " text = re.sub(r'[^A-Za-z0-9\\s]+', '', text) # this removes special characters\n", + " text = re.sub(r'\\s+', ' ', text).strip() # this normalizes whitespace\n", + " return text\n", + "\n", + "df[\"clean_text\"] = df[\"text\"].apply(clean_text)\n", + "df.head(10)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/02_training.ipynb b/notebooks/02_training.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..1766fe94d4c37d4b74a1e37bc496b0e5a53bb391 --- /dev/null +++ b/notebooks/02_training.ipynb @@ -0,0 +1,368 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "80f816c1-0839-41cb-847b-c79a62ca1465", + "metadata": {}, + "source": [ + "### Load all required modules for loading data, model setup, training, and metric evaluation" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "2554d05b-f08a-4c21-953f-4f507407e426", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import os\n", + "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), \"..\", \"src\")))\n", + "from data_loader import load_and_prepare_data \n", + "from model import get_model, get_tokenizer \n", + "from train import get_training_args, train_model \n", + "from evaluate import compute_metrics \n", + "from torch.utils.data import Dataset \n", + "import torch" + ] + }, + { + "cell_type": "markdown", + "id": "3bfbb706-4b0b-43de-a95a-884d46343668", + "metadata": {}, + "source": [ + "### Define a class that wraps tokenized data and labels for Hugging Face’s Trainer to use" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c814c354-7962-4a2d-b7bd-5c498f1d004e", + "metadata": {}, + "outputs": [], + "source": [ + "class EmotionDataset(Dataset):\n", + " def __init__(self, encodings, labels):\n", + " self.encodings = encodings # BERT tokenized inputs (input_ids, attention_mask)\n", + " self.labels = labels # Encoded labels (integers)\n", + "\n", + " def __len__(self):\n", + " return len(self.labels) # Total number of samples\n", + "\n", + " def __getitem__(self, idx):\n", + " # Return dictionary of input tensors + label tensor for a single sample\n", + " return {\n", + " key: torch.tensor(val[idx]) for key, val in self.encodings.items()\n", + " } | {\"labels\": torch.tensor(self.labels[idx])}" + ] + }, + { + "cell_type": "markdown", + "id": "f9b87257-f0c0-4532-9eee-939d8747ef79", + "metadata": {}, + "source": [ + "### Load the dataset from Hugging Face, clean and encode it, then tokenize it using the BERT tokenizer." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "18e312be-5863-4e24-900a-843e42e145cc", + "metadata": {}, + "outputs": [], + "source": [ + "# Load train/test splits and label encoder\n", + "train_texts, test_texts, train_labels, test_labels, label_encoder = load_and_prepare_data()\n", + "\n", + "# Load BERT tokenizer\n", + "tokenizer = get_tokenizer()\n", + "\n", + "# Tokenize training and testing texts with truncation and padding\n", + "train_encodings = tokenizer(train_texts, truncation=True, padding=True, max_length=128)\n", + "test_encodings = tokenizer(test_texts, truncation=True, padding=True, max_length=128)\n", + "\n", + "# Wrap the tokenized data into EmotionDataset objects\n", + "train_dataset = EmotionDataset(train_encodings, train_labels)\n", + "test_dataset = EmotionDataset(test_encodings, test_labels)" + ] + }, + { + "cell_type": "markdown", + "id": "66b99b4e-5297-4bc0-8cfb-20dbe22526c0", + "metadata": {}, + "source": [ + "### Samples from the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "35db4426-db21-4438-ba0e-ebb51d52edfb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sample 1\n", + "Text: i'd just feel less out of place, i guess. my sa makes me feel like i'm so behind my peers in terms of a social life\n", + "Label (encoded): 9\n", + "\n", + "Sample 2\n", + "Text: i love the lady in the green jacket chasing after the second car looking back at the first car like \"look what you did\"\n", + "Label (encoded): 18\n", + "\n", + "Sample 3\n", + "Text: man. really bad last possession there. bummer.\n", + "Label (encoded): 10\n", + "\n", + "Sample 4\n", + "Text: never would’ve guessed that one.\n", + "Label (encoded): 20\n", + "\n", + "Sample 5\n", + "Text: i wasn’t even expecting the reply that’s why i’m literally bamboozled.\n", + "Label (encoded): 27\n", + "\n" + ] + } + ], + "source": [ + "for i in range(5):\n", + " print(f\"Sample {i+1}\")\n", + " print(f\"Text: {train_texts[i]}\")\n", + " print(f\"Label (encoded): {train_labels[i]}\")\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "id": "0883760a-a449-42ca-ba69-fa01d874e50b", + "metadata": {}, + "source": [ + "### Set up the BERT model for sequence classification and define training parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "3176ccf4-d20d-460c-b620-c73a1ab9cb6d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n", + "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n", + "/opt/anaconda3/lib/python3.12/site-packages/transformers/training_args.py:1545: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "# Load pre-trained BERT model with classification head for number of emotion classes\n", + "model = get_model(num_labels=len(label_encoder.classes_))\n", + "\n", + "# Set training configuration: batch size, epochs, logging, saving, evaluation\n", + "training_args = get_training_args()" + ] + }, + { + "cell_type": "markdown", + "id": "874a4e6a-80dd-470d-9283-e1c88e731b8e", + "metadata": {}, + "source": [ + "### Train the Model " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4c312e56-52bf-417d-82c0-8a1f47b82670", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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EpochTraining LossValidation LossAccuracyF1
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EpochTraining LossValidation LossAccuracyF1
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" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "TrainOutput(global_step=5448, training_loss=0.7054264770818002, metrics={'train_runtime': 5721.3012, 'train_samples_per_second': 15.23, 'train_steps_per_second': 0.952, 'total_flos': 5733080823638016.0, 'train_loss': 0.7054264770818002, 'epoch': 3.0})" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trainer = train_model(\n", + " model=model,\n", + " args=training_args,\n", + " train_dataset=train_dataset,\n", + " val_dataset=test_dataset,\n", + " compute_metrics=compute_metrics\n", + ")\n", + "\n", + "# Begin training\n", + "trainer.train()" + ] + }, + { + "cell_type": "markdown", + "id": "020729b6-c545-42ba-bd2c-00ee5f9bbb80", + "metadata": {}, + "source": [ + "### Save both model weights and tokenizer files for future inference or deployment." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "5f12aedb-b3f8-4a1b-8e1f-6a68eb29933f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('../outputs/model/tokenizer_config.json',\n", + " '../outputs/model/special_tokens_map.json',\n", + " '../outputs/model/vocab.txt',\n", + " '../outputs/model/added_tokens.json')" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pathlib import Path\n", + "model_path = Path(\"..\") / \"outputs\" / \"model\"\n", + "model.save_pretrained(model_path)\n", + "tokenizer.save_pretrained(model_path)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/03_evaluation.ipynb b/notebooks/03_evaluation.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..fb9cced2549160371df3404d962093079991d639 --- /dev/null +++ b/notebooks/03_evaluation.ipynb @@ -0,0 +1,708 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "5df279f6-36ad-4b03-b31e-3f29467f466c", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import os\n", + "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), \"..\", \"src\")))\n", + "from data_loader import load_and_prepare_data\n", + "from evaluate import save_confusion_matrix, save_metrics\n", + "from model import get_tokenizer\n", + "from transformers import BertForSequenceClassification\n", + "import torch\n", + "import numpy as np\n", + "from pathlib import Path\n", + "from transformers import Trainer\n", + "from captum.attr import IntegratedGradients, LayerIntegratedGradients, visualization\n", + "from transformers import BertTokenizer, BertForSequenceClassification\n", + "import random\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as mpimg\n", + "from IPython.display import display, Image, HTML" + ] + }, + { + "cell_type": "markdown", + "id": "a2994399-f54d-4d01-85fd-7437055d53d2", + "metadata": {}, + "source": [ + "### Reload Saved Model and Tokenizer" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c3fd3a1e-57fd-4450-abb7-ce547f2544b1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "BertForSequenceClassification(\n", + " (bert): BertModel(\n", + " (embeddings): BertEmbeddings(\n", + " (word_embeddings): Embedding(30522, 768, padding_idx=0)\n", + " (position_embeddings): Embedding(512, 768)\n", + " (token_type_embeddings): Embedding(2, 768)\n", + " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " (encoder): BertEncoder(\n", + " (layer): ModuleList(\n", + " (0-11): 12 x BertLayer(\n", + " (attention): BertAttention(\n", + " (self): BertSdpaSelfAttention(\n", + " (query): Linear(in_features=768, out_features=768, bias=True)\n", + " (key): Linear(in_features=768, out_features=768, bias=True)\n", + " (value): Linear(in_features=768, out_features=768, bias=True)\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " (output): BertSelfOutput(\n", + " (dense): Linear(in_features=768, out_features=768, bias=True)\n", + " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " )\n", + " (intermediate): BertIntermediate(\n", + " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", + " (intermediate_act_fn): GELUActivation()\n", + " )\n", + " (output): BertOutput(\n", + " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", + " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " )\n", + " (pooler): BertPooler(\n", + " (dense): Linear(in_features=768, out_features=768, bias=True)\n", + " (activation): Tanh()\n", + " )\n", + " )\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " (classifier): Linear(in_features=768, out_features=28, bias=True)\n", + ")" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_path = Path(\"..\") / \"outputs\" / \"model\"\n", + "\n", + "# Load trained model and tokenizer\n", + "model = BertForSequenceClassification.from_pretrained(model_path)\n", + "tokenizer = get_tokenizer().from_pretrained(model_path)\n", + "model.eval()" + ] + }, + { + "cell_type": "markdown", + "id": "eed752f5-f464-4546-9f96-84ab3487efd4", + "metadata": {}, + "source": [ + "### Prepare Validation Data for Evaluation" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "29f9d9d9-1c5f-49e0-b0b1-e48150926256", + "metadata": {}, + "outputs": [], + "source": [ + "# Reload test split and labels using same preprocessing logic as training\n", + "train_texts, test_texts, train_labels, test_labels, label_encoder = load_and_prepare_data()\n", + "\n", + "# Tokenize test texts\n", + "test_encodings = tokenizer(test_texts, truncation=True, padding=True, max_length=128)\n", + "\n", + "# Wrap validation data in custom dataset class\n", + "from torch.utils.data import Dataset\n", + "\n", + "class EmotionDataset(Dataset):\n", + " def __init__(self, encodings, labels):\n", + " self.encodings = encodings\n", + " self.labels = labels\n", + "\n", + " def __len__(self):\n", + " return len(self.labels)\n", + "\n", + " def __getitem__(self, idx):\n", + " return {\n", + " key: torch.tensor(val[idx]) for key, val in self.encodings.items()\n", + " } | {\"labels\": torch.tensor(self.labels[idx])}\n", + "\n", + "test_dataset = EmotionDataset(test_encodings, test_labels)" + ] + }, + { + "cell_type": "markdown", + "id": "fc1a891f-8742-4e35-a16b-a0dd14f58f51", + "metadata": {}, + "source": [ + "### Generate Predictions using the Hugging Face Trainer" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b84c7173-89c5-43fd-9abf-88434d987d08", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Initialize Hugging Face Trainer with the loaded model\n", + "trainer = Trainer(model=model)\n", + "\n", + "# Generate raw predictions\n", + "predictions = trainer.predict(test_dataset)\n", + "\n", + "# Extract labels and predicted classes\n", + "y_true = predictions.label_ids\n", + "y_pred = np.argmax(predictions.predictions, axis=1)" + ] + }, + { + "cell_type": "markdown", + "id": "521ddf99-70e4-4a41-8df8-3674118f7afe", + "metadata": {}, + "source": [ + "### Generate the Classification Report" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "64fed2bb-e5d3-4d9a-8c02-6a6ceb2c748c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Classification Report:\n", + "\n", + " precision recall f1-score support\n", + "\n", + " admiration 0.67 0.76 0.71 542\n", + " amusement 0.73 0.85 0.78 330\n", + " anger 0.43 0.55 0.48 205\n", + " annoyance 0.30 0.19 0.23 290\n", + " approval 0.38 0.37 0.38 375\n", + " caring 0.38 0.48 0.42 130\n", + " confusion 0.50 0.42 0.46 172\n", + " curiosity 0.46 0.60 0.52 278\n", + " desire 0.64 0.53 0.58 78\n", + "disappointment 0.33 0.20 0.25 142\n", + " disapproval 0.32 0.48 0.39 280\n", + " disgust 0.51 0.45 0.48 100\n", + " embarrassment 0.79 0.56 0.66 41\n", + " excitement 0.42 0.31 0.36 102\n", + " fear 0.81 0.50 0.62 86\n", + " gratitude 0.91 0.88 0.90 371\n", + " grief 0.00 0.00 0.00 8\n", + " joy 0.55 0.58 0.56 171\n", + " love 0.78 0.85 0.81 285\n", + " nervousness 0.46 0.35 0.40 17\n", + " neutral 0.68 0.66 0.67 2565\n", + " optimism 0.67 0.56 0.61 172\n", + " pride 0.33 0.10 0.15 10\n", + " realization 0.56 0.15 0.24 117\n", + " relief 0.00 0.00 0.00 17\n", + " remorse 0.61 0.85 0.71 71\n", + " sadness 0.55 0.50 0.53 163\n", + " surprise 0.57 0.66 0.61 144\n", + "\n", + " accuracy 0.60 7262\n", + " macro avg 0.51 0.48 0.48 7262\n", + " weighted avg 0.60 0.60 0.60 7262\n", + "\n" + ] + } + ], + "source": [ + "from sklearn.metrics import classification_report\n", + "\n", + "# Create and print the classification report\n", + "report = classification_report(\n", + " y_true, \n", + " y_pred, \n", + " target_names=label_encoder.classes_,\n", + " zero_division=0\n", + ")\n", + "print(\"Classification Report:\\n\")\n", + "print(report)\n", + "\n", + "# Also save it as JSON\n", + "from sklearn.metrics import classification_report\n", + "import json\n", + "from pathlib import Path\n", + "\n", + "metrics_dir = Path(\"..\") / \"outputs\" / \"metrics\"\n", + "metrics_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + "# Save the report in dictionary form for precision/recall/F1\n", + "report_dict = classification_report(\n", + " y_true, \n", + " y_pred, \n", + " target_names=label_encoder.classes_, \n", + " output_dict=True,\n", + " zero_division=0)\n", + "\n", + "with open(metrics_dir / \"report.json\", \"w\") as f:\n", + " json.dump(report_dict, f, indent=4)" + ] + }, + { + "cell_type": "markdown", + "id": "e9d1b6e7-08cf-424d-8a01-afc59f5f7cea", + "metadata": {}, + "source": [ + "### Plot the Confusion Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "a7ed7ed2-c295-4819-8cf6-e0ea4b0efbfa", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Compute confusion matrix\n", + "cm = confusion_matrix(y_true, y_pred)\n", + "\n", + "# Plot it\n", + "plt.figure(figsize=(12, 8))\n", + "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=label_encoder.classes_, yticklabels=label_encoder.classes_)\n", + "plt.xlabel(\"Predicted\")\n", + "plt.ylabel(\"True\")\n", + "plt.title(\"Confusion Matrix\")\n", + "plt.xticks(rotation=45)\n", + "plt.yticks(rotation=0)\n", + "plt.tight_layout()\n", + "plt.savefig(metrics_dir / \"confusion_matrix.png\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "88f43efd-2b75-4d13-86dd-ad7f52cc3fb2", + "metadata": {}, + "source": [ + "## Interpretability with Captum\n", + "\n", + "In this section, we apply the Integrated Gradients method from Captum to better understand how the model makes its predictions. \n", + "We randomly select 5 validation samples, visualize token importance, and analyze how different words contribute to the model’s decision-making." + ] + }, + { + "cell_type": "markdown", + "id": "d856417e-03a6-4b26-98a1-1ab11c07695e", + "metadata": {}, + "source": [ + "### Interpretation Summary\n", + "\n", + "The above visualizations highlight which words in each text sample had the most influence on the model’s prediction. \n", + "Larger importance values (darker colors or taller bars) indicate stronger contribution to the decision. \n", + "This helps us verify whether the model is focusing on relevant keywords when classifying emotions." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e8d55198-c708-4659-93b9-30f2ca3a098e", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the custom forward function\n", + "def forward_func(inputs_embeds, attention_mask):\n", + " outputs = model(inputs_embeds=inputs_embeds, attention_mask=attention_mask)\n", + " return torch.softmax(outputs.logits, dim=1)\n", + "\n", + "# Initialize Integrated Gradients\n", + "ig = IntegratedGradients(forward_func)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "2935c1d5-9811-4825-8f68-8594d7728f97", + "metadata": {}, + "outputs": [], + "source": [ + "# Pick 5 random samples\n", + "sample_indices = random.sample(range(len(test_texts)), 5)\n", + "sample_texts = [test_texts[i] for i in sample_indices]\n", + "sample_labels = [test_labels[i] for i in sample_indices]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "fd160f66-9c1f-4859-884b-1536a7da498f", + "metadata": {}, + "outputs": [], + "source": [ + "# Define helper functions for plotting\n", + "def plot_heatmap(tokens, attributions, label_name, idx):\n", + " \"\"\"\n", + " Plots and saves a heatmap of token attributions.\n", + " \"\"\"\n", + " attributions_np = attributions.detach().cpu().numpy()\n", + " fig, ax = plt.subplots(figsize=(len(tokens) * 0.6, 2))\n", + " ax.bar(range(len(tokens)), attributions_np, color='lightblue')\n", + " ax.set_xticks(range(len(tokens)))\n", + " ax.set_xticklabels(tokens, rotation=45, ha=\"right\", fontsize=8)\n", + " ax.set_title(f\"Sample {idx+1} - True Label: {label_name}\", fontsize=10)\n", + " plt.tight_layout()\n", + " plt.savefig(f\"../outputs/interpretations/sample_{idx+1}_{label_name}_heatmap.png\")\n", + " plt.show()\n", + "\n", + "def plot_bar(tokens, attributions, label_name, idx):\n", + " \"\"\"\n", + " Plots and saves a horizontal bar chart of token attributions.\n", + " \"\"\"\n", + " attributions_np = attributions.detach().cpu().numpy()\n", + " tokens_np = np.array(tokens)\n", + " fig, ax = plt.subplots(figsize=(6, len(tokens) * 0.4))\n", + " ax.barh(tokens_np, attributions_np, color='salmon')\n", + " ax.set_title(f\"Attribution Scores (Bar) - Sample {idx+1}: {label_name}\", fontsize=10)\n", + " ax.invert_yaxis()\n", + " plt.tight_layout()\n", + " plt.savefig(f\"../outputs/interpretations/sample_{idx+1}_{label_name}_bar.png\")\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "c6b7ee4b-67d6-4ca4-9bae-2ea35c5af9d9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "BertForSequenceClassification(\n", + " (bert): BertModel(\n", + " (embeddings): BertEmbeddings(\n", + " (word_embeddings): Embedding(30522, 768, padding_idx=0)\n", + " (position_embeddings): Embedding(512, 768)\n", + " (token_type_embeddings): Embedding(2, 768)\n", + " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " (encoder): BertEncoder(\n", + " (layer): ModuleList(\n", + " (0-11): 12 x BertLayer(\n", + " (attention): BertAttention(\n", + " (self): BertSdpaSelfAttention(\n", + " (query): Linear(in_features=768, out_features=768, bias=True)\n", + " (key): Linear(in_features=768, out_features=768, bias=True)\n", + " (value): Linear(in_features=768, out_features=768, bias=True)\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " (output): BertSelfOutput(\n", + " (dense): Linear(in_features=768, out_features=768, bias=True)\n", + " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " )\n", + " (intermediate): BertIntermediate(\n", + " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", + " (intermediate_act_fn): GELUActivation()\n", + " )\n", + " (output): BertOutput(\n", + " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", + " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " )\n", + " (pooler): BertPooler(\n", + " (dense): Linear(in_features=768, out_features=768, bias=True)\n", + " (activation): Tanh()\n", + " )\n", + " )\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " (classifier): Linear(in_features=768, out_features=28, bias=True)\n", + ")" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "device = torch.device(\"cpu\")\n", + "model.to(device)\n", + "model.eval()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "76359edf-453f-4afe-9b74-ea5bcb5d3066", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved interpretation plots for sample 1: 'disapproval'\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved interpretation plots for sample 2: 'neutral'\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved interpretation plots for sample 3: 'neutral'\n" + ] + }, + { + "data": { + "image/png": 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nPfXu27dPCxcu9NSyZcsWo3Yk6ffff1daWprn/f2rwMBA1a1b17i9P2vcuLEked6LzZs3a8GCBRowYIAkKSMjQ0eOHLmotv8qPT1dhw4d8nzSbfv27fLx8VH9+vXzrctutys9PV3du3c3an/YsGHq3bt3nm19+/bVsGHD8v2zdSGBgYG69tprde211+r+++9Xw4YNtXfvXlmWpSNHjuipp55SVFSUJGnXrl3n1f7X6Uf++nf479SoUUO1atXSd999p9tuu63A40JDQzVkyBANGTJEN954o/r166fffvtNVapUKbAPrVu3LlQtKF0ITigT1qxZo6NHj2rkyJFyOp159t144416+eWXNWbMGMXExOj7779XSkqKLrvsMoWEhMhutysmJkbr169Xly5dZLfbCz06tXXrVs2aNUvXXXed1q1bpzfeeEPvvvuupD/+ge/YsaOeeuopxcTE6MiRI5o6dWqe86Ojo2Wz2bRmzRoNGDBAgYGBnlGXc+rVq6dBgwZp1KhRWrhwoUJCQjR58mTVqlVLgwYNuohX7Q/Tp0/XqVOnNGDAAEVHR+vYsWOaN2+esrOz1adPH0nStGnT1KtXL9WpU0e33HKLcnJy9N5772nSpEkXXVf9+vV12223afjw4UpKSlKrVq105MgRffzxx2rWrJkGDBigcePGqX///qpfv76OHj2qjz/++IJhrG/fvrrrrrt09uzZ80axDh8+rN9//11ut1ufffaZli1bphtvvFGSVLlyZYWHh+ull15SZGSk0tPTNXnyZOPX8NNPP/WM/FyKe++9VzVr1lTPnj112WWXKTMzUzNnzlS1atU8bdetW1fLli1T27Zt5XK59PDDD19wRLAwAgICNGLECD377LNyuVwaO3asbr75ZkVERJx3bEhIiCZOnKiHHnpIubm56tq1q1wul7Zt2yaHw6ERI0acd054eLjCw8PzbPP391dERIQaNGjg2RYfH6///e9/Bd6uW7p0qc6ePasOHTooKChIy5YtU2BgoKKjo5Wbm6tKlSpp/vz5Gj16tL788svz5kIbPXq0kpKSNH78eN1zzz1KTk7W0qVLC/16TZ8+XWPHjlVoaKj69+8vt9utXbt26ejRoxo/frxmz56tyMhItWzZUj4+PnrjjTcUERGhsLCwC/YBZZyXnq0CCmXgwIHWgAED8t2XnJxsSbKSk5Ot33//3brhhhussLAwzyfpLMuyVq9ebdWtW9fy8/M7bzqCv8rv4fCEhATr5ptvtoKCgqwaNWqc9wDxuU8xBQYGWi1btrQ+/PDDPA+HW5ZlzZgxw4qIiLBsNtvfTkfgdDqtwMBAq2/fvvl+7P/P3n77betCf5U//vhj64YbbvB8LLpGjRpWv379rM2bN+c5buXKlVbLli2tSpUqWVWrVrUGDx58SXVZlmWdOXPGeuyxx6yYmBjL39/fioiIsK6//nprz549lmVZ1pgxY6w6depYdrvdqlatmjVs2DDryJEjBfYlJyfHqlWrlvX+++97tp17OPzc4ufnZ11++eXWxIkTrRMnTniOW7dundWoUSPLbrdbzZs3tzZu3JjnQwP5PeR/zt13323dc889BdZl6s0337QGDBhgRUZGWpUqVbJq1qxp3XDDDZ7Xw7Isa/fu3Vbbtm0tu91u1atXz3rjjTfOe8D6z3UXVPtfP5Bw7s/7ggULrJo1a1oBAQHW4MGDrd9++81zTn7TEcydO9dq0KCB5e/vb1WrVs3q27fvBT/5+FcX86m6t99+2+rQoYMVGhpqBQcHWx07drQ++ugjz/4VK1ZYMTExlt1utzp16mStXr36vP6/8847Vt26dS273W5dccUV1uLFi/OdjuCv1/3r36Xly5d7/l5UrlzZ6tatm/XWW29ZlvXHp+5atmxpBQcHW6GhoVavXr2s3bt3G/UBZZfNsgxvBgNAKbBgwQL997//1QcffFAi1/vll1/UsGFD7dq1y/NAeVk0ffp0rVq16rzbyAAKh1t1AMqUu+++W0ePHtXx48dLZIb377//XgsWLCjToQlA0SE4AShT/Pz8NGXKlBK7Xvv27dW+ffsSux6A0o1bdQAAAIaYABMAAMAQwQkAAMAQwQkAAMAQD4f/SW5urg4dOqSQkJALfs8SAAAoPyzL0vHjx1WzZk3P12gVhOD0J4cOHfJM4Q8AACqWjIyMPF+EnR+C05+cmxMmIyNDoaGhXq4GAACUBJfLpaioKKO54QhOf3Lu9lxoaCjBCQCACsbkMR0eDgcAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADDk5+0CcL7shAnGx/pPSyrGSgAAwJ8x4gQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCo2IJTjx49ZLPZZLPZlJKSUlyXyVdMTIzn2seOHSvRawMAgPKrWEecRo0apczMTDVt2tSzbeXKlerRo4ecTqccDoeaN2+uGTNm6LfffpMkLV26VGFhYQW2efjwYd1zzz2qXbu27Ha7IiIi1LdvX23fvt1zzM6dO7Vy5cpi6xcAAKiYijU4BQUFKSIiQn5+f3yzy5QpUzRkyBC1a9dO7733nr788kslJSXpiy++0LJly4zavOGGG/TFF1/olVde0TfffKPVq1erR48enuAlSdWqVVOVKlWKpU8AAKDiKrHvqvvss8/05JNPas6cOXrwwQc922NiYtSnTx+jW2rHjh3Tli1btHHjRnXv3l2SFB0drfbt2xdX2QAAAB4l9nD48uXL5XA4dN999+W7/0K3585xOBxyOBxatWqV3G53EVcIAABwYSUWnA4cOKDY2Fj5+/tfdBt+fn5aunSpXnnlFYWFhalLly569NFHtWfPnotqz+12y+Vy5VkAAAAKUmLBybIs2Wy2S27nhhtu0KFDh7R69Wr17dtXGzduVOvWrbV06dJCt5WYmCin0+lZoqKiLrk+AABQfpVYcKpfv77S0tKUnZ19yW0FBASoT58+euyxx7Rt2zbFxcVp2rRphW4nPj5eWVlZniUjI+OSawMAAOVXiQWnoUOH6sSJE1qwYEG++y9lvqXGjRvr5MmThT7PbrcrNDQ0zwIAAFCQEvtUXYcOHTRp0iRNmDBB//vf/3T99derZs2a+vbbb/Xiiy+qa9eunk/bnT179rxJMytVqqQaNWropptu0p133qnmzZsrJCREu3bt0qxZszRo0KCS6goAAKigSiw4SdLTTz+tNm3a6B//+IdefPFF5ebmqk6dOrrxxhs1YsQIz3EnTpxQq1at8pwbHR2t/fv3q0OHDpo9e7bntl9UVJRGjRqlRx99tCS7AgAAKqASDU6SdPPNN+vmm28ucH9cXJzi4uIK3J+YmKjExMRiqAwAAODCivUZpwULFsjhcGjv3r3FeZnzNGnSRP379y/RawIAgPKv2Eacli9frtOnT0uSateuXVyXydfatWs9n97jgW8AAFBUii041apVq7ia/lvR0dFeuzYAACi/Smw6AgAAgLKO4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCoxGcOx9/zn5bk7RIAAEA+GHECAAAwRHACAAAwRHACAAAwRHACAAAwRHACAAAwRHACAAAwRHACAAAwxDxOJSg7YYLRcczjBABA6cSIEwAAgCGCEwAAgCGCEwAAgCGCEwAAgCGCEwAAgCGCEwAAgCGCEwAAgCGCEwAAgCGCEwAAgCGCEwAAgCGCEwAAgCGCEwAAgKFyF5zef/99de3aVWFhYQoPD9fAgQOVlpbm7bIAAEA5UO6C08mTJzV+/Hjt3LlT69evl4+Pj66//nrl5uaed6zb7ZbL5cqzAAAAFMTP2wUUtRtuuCHP+ssvv6zq1asrNTVVTZs2zbMvMTFRCQkJJVkeAAAow8rdiFNaWpqGDh2q2NhYhYaG6vLLL5ckpaenn3dsfHy8srKyPEtGRkZJlwsAAMqQcjfidM011ygqKkqLFi1SzZo1lZubq6ZNm+rMmTPnHWu322W3271QJQAAKIvKVXD69ddftW/fPi1cuFBXXHGFJGnLli1ergoAAJQX5So4Va5cWeHh4XrppZcUGRmp9PR0TZ482dtlAQCAcqJcPePk4+Oj119/XcnJyWratKkeeughPfPMM94uCwAAlBPlasRJknr37q3U1NQ82yzL8lI1AACgPClXI04AAADFieAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgqNxNgFma+U9L8nYJAADgEjDiBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIh5nEqh7IQJl3Q+80UBAFA8GHECAAAwRHACAAAwRHACAAAwRHACAAAwRHACAAAwRHACAAAwRHACAAAwRHACAAAwRHACAAAwVCaC08aNG2Wz2XTs2DFvlwIAACqwUhmcevTooXHjxnm7DAAAgDxKZXACAAAojUpdcIqLi9OmTZs0d+5c2Ww22Ww2HTx4UJKUnJystm3bKigoSJ07d9b+/fvznPvOO++oTZs2CggIUGxsrBISEpSTk+OFXgAAgPKo1AWnuXPnqlOnTho1apQyMzOVmZmpqKgoSdKUKVOUlJSkXbt2yc/PT3feeafnvA8++EC33367xo4dq9TUVC1cuFBLly7VE088UeC13G63XC5XngUAAKAgpS44OZ1OVapUSUFBQYqIiFBERIR8fX0lSU888YS6d++uxo0ba/Lkydq2bZt+//13z77JkydrxIgRio2NVZ8+ffT4449r4cKFBV4rMTFRTqfTs5wLaAAAAPkpdcHpQpo3b+75OTIyUpJ0+PBhSX/cxpsxY4YcDodnOTdqderUqXzbi4+PV1ZWlmfJyMgo/k4AAIAyy8/bBRSGv7+/52ebzSZJys3N9fw3ISFBgwcPPu+8gICAfNuz2+2y2+3FUCkAACiPSmVwqlSpks6ePVuoc1q3bq39+/erbt26xVQVAACo6EplcIqJidGOHTt08OBBORwOz6jShTz22GMaOHCgoqKidNNNN8nHx0d79uzR3r17NXPmzBKoGgAAlHel8hmniRMnytfXV40bN1a1atWUnp7+t+f07dtXa9as0bp169SuXTt17NhRzz33nKKjo0ugYgAAUBHYLMuyvF1EaeFyueR0OpWVlaXQ0FCv1ZGdMOGSzvefllRElQAAUP4V5vd/qRxxAgAAKI0ITgAAAIYITgAAAIYITgAAAIYITgAAAIYITgAAAIYITgAAAIYITgAAAIYITgAAAIZK5XfVVXTM/A0AQOnEiBMAAIAhghMAAIAhghMAAIAhghMAAIAhghMAAIAhghMAAIAhglMpk50wwdslAACAAhCcAAAADBGcAAAADBGcAAAADBGcAAAADBGcAAAADBGcAAAADBGcAAAADBGcAAAADBGcAAAADBGcAAAADBGcAAAADBGcAAAADBGcAAAADBGcAAAADPl5uwBvcrvdcrvdnnWXy+XFagAAQGlXoUecEhMT5XQ6PUtUVJS3SwIAAKVYhQ5O8fHxysrK8iwZGRneLgkAAJRiFfpWnd1ul91u93YZAACgjKjQI04AAACFQXACAAAwRHACAAAwVK6CU3p6uhwOR4FLenq6t0sEAABlWLl6OLxmzZpKSUm54H4AAICLVa6Ck5+fn+rWrevtMgAAQDlVrm7VAQAAFCeCEwAAgCGCEwAAgCGCEwAAgCGCEwAAgCGCEwAAgCGCUynjPy3J2yUAAIACEJwAAAAMEZwAAAAMEZwAAAAMEZwAAAAMEZwAAAAMEZwAAAAMEZxKkeyECcpOmODtMgAAQAEITgAAAIYITgAAAIYITgAAAIYITgAAAIYITgAAAIYITgAAAIYITgAAAIYITgAAAIYITgAAAIaKLDj16NFD48aNK6rmjNhsNq1atarA/QcPHpTNZlNKSkqJ1QQAAMovRpwAAAAMEZwAAAAMXVRwOnnypIYPHy6Hw6HIyEglJSXl2X/06FENHz5clStXVlBQkPr3768DBw549i9dulRhYWH64IMP1KhRIzkcDvXr10+ZmZmeY3bu3Kk+ffqoatWqcjqd6t69u3bv3n3Buj777DO1atVKAQEBatu2rT7//POL6R4AAEC+Lio4Pfzww9qwYYPefvttffjhh9q4caOSk5M9++Pi4rRr1y6tXr1a27dvl2VZGjBggLKzsz3HnDp1Ss8++6yWLVumTz75ROnp6Zo4caJn//HjxzVixAht3rxZn376qerVq6cBAwbo+PHj+dZ08uRJDRw4UA0aNFBycrKmT5+epz0AAIBL5VfYE06cOKGXX35Zr776qvr06SNJeuWVV3TZZZdJkg4cOKDVq1dr69at6ty5syRp+fLlioqK0qpVq3TTTTdJkrKzs/Xiiy+qTp06kqQxY8ZoxowZnuv07Nkzz3UXLlyoypUra9OmTRo4cOB5dS1fvlxnz57V4sWLFRQUpCZNmujHH3/UvffeW2Bf3G633G63Z93lchX25QAAABVIoUec0tLSdObMGXXq1MmzrUqVKmrQoIEkad++ffLz81OHDh08+8PDw9WgQQPt27fPsy0oKMgTmiQpMjJShw8f9qwfPnxYo0ePVv369eV0OuV0OnXixAmlp6fnW9e+ffvUokULBQUFebb9ucb8JCYmetp2Op2KiooyfBUAAEBFVOjgZFnWRe23LEs2m82z7u/vn2e/zWbLc25cXJySk5M1Z84cbdu2TSkpKQoPD9eZM2cuqq78xMfHKysry7NkZGQUug0AAFBxFDo41a1bV/7+/vr00089244ePapvvvlGktS4cWPl5ORox44dnv2//vqrvvnmGzVq1Mj4Ops3b9bYsWM1YMAANWnSRHa7XUeOHCnw+MaNG+uLL77Q6dOnPdv+XGN+7Ha7QkND8ywAAAAFKXRwcjgcGjlypB5++GGtX79eX375peLi4uTj80dT9erV06BBgzRq1Cht2bJFX3zxhW6//XbVqlVLgwYNMr5O3bp1tWzZMu3bt087duzQbbfdpsDAwAKPHzp0qHx8fDRy5EilpqZq7dq1evbZZwvbPQAAgAJd1KfqnnnmGXXr1k3XXnutevfura5du6pNmzae/UuWLFGbNm00cOBAderUSZZlae3atefdnruQxYsX6+jRo2rVqpWGDRumsWPHqnr16gUe73A49M477yg1NVWtWrXSlClT9PTTT19M9wAAAPJlsy7m4aByyuVyyel0Kisryyu37bITJkiS/Kcl/c2RAACgqBTm9z8zhwMAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABjy83YB+D/MGA4AQOnGiBMAAIAhghMAAIAhghMAAIAhghMAAIAhghMAAIAhghMAAIAhghMAAIAh5nHyouyECfluZz4nAABKJ0acAAAADBGcAAAADBGcAAAADBGcAAAADBGcAAAADBGcAAAADBGcAAAADBGcAAAADBGcAAAADJXa4LRx40bZbDYdO3bM26UAAABIKsXBqahYlqWcnBxvlwEAAMqBSwpOPXr00JgxYzRmzBiFhYUpPDxcU6dOlWVZkqSjR49q+PDhqly5soKCgtS/f38dOHDAc/4PP/yga665RpUrV1ZwcLCaNGmitWvX6uDBg7ryyislSZUrV5bNZlNcXJykP4LQrFmzFBsbq8DAQLVo0UJvvvmmp81zI1UffPCB2rZtK7vdrs2bN19KNwEAACQVwZf8vvLKKxo5cqR27NihXbt26e6771Z0dLRGjRqluLg4HThwQKtXr1ZoaKgeeeQRDRgwQKmpqfL399f999+vM2fO6JNPPlFwcLBSU1PlcDgUFRWllStX6oYbbtD+/fsVGhqqwMBASdLUqVP11ltv6YUXXlC9evX0ySef6Pbbb1e1atXUvXt3T12TJk3Ss88+q9jYWIWFhV1qNwEAAC49OEVFRWn27Nmy2Wxq0KCB9u7dq9mzZ6tHjx5avXq1tm7dqs6dO0uSli9frqioKK1atUo33XST0tPTdcMNN6hZs2aSpNjYWE+7VapUkSRVr17dE3xOnjyp5557Th9//LE6derkOWfLli1auHBhnuA0Y8YM9enT54K1u91uud1uz7rL5brUlwMAAJRjl/yMU8eOHWWz2TzrnTp10oEDB5Samio/Pz916NDBsy88PFwNGjTQvn37JEljx47VzJkz1aVLF02bNk179uy54LVSU1P1+++/q0+fPnI4HJ7l1VdfVVpaWp5j27Zt+7e1JyYmyul0epaoqKjCdB0AAFQwJf5wuGVZnqB111136bvvvtOwYcO0d+9etW3bVvPnzy/w3NzcXEnSu+++q5SUFM+Smpqa5zknSQoODv7bWuLj45WVleVZMjIyLqFnAACgvLvk4PTpp5+et16vXj01btxYOTk52rFjh2ffr7/+qm+++UaNGjXybIuKitLo0aP11ltvacKECVq0aJEkqVKlSpKks2fPeo5t3Lix7Ha70tPTVbdu3TzLxYwW2e12hYaG5lkAAAAKcsnPOGVkZGj8+PG65557tHv3bs2fP19JSUmqV6+eBg0apFGjRmnhwoUKCQnR5MmTVatWLQ0aNEiSNG7cOPXv31/169fX0aNH9fHHH3tCVXR0tGw2m9asWaMBAwYoMDBQISEhmjhxoh566CHl5uaqa9eucrlc2rZtmxwOh0aMGHGp3QEAACjQJQen4cOH6/Tp02rfvr18fX31wAMP6O6775YkLVmyRA8++KAGDhyoM2fOqFu3blq7dq38/f0l/TGadP/99+vHH39UaGio+vXrp9mzZ0uSatWqpYSEBE2ePFl33HGHhg8frqVLl+rxxx9X9erVlZiYqO+++05hYWFq3bq1Hn300UvtCgAAwAXZrHOTLl2EHj16qGXLlpozZ04RluQ9LpdLTqdTWVlZJXLbLjthQr7b/aclFfu1AQDAHwrz+7/czxwOAABQVAhOAAAAhi7pGaeNGzcWURkAAAClHyNOAAAAhghOAAAAhghOAAAAhghOAAAAhghOAAAAhghOAAAAhi75K1dw8ZghHACAsoURJwAAAEMEJwAAAEMEJwAAAEMEJwAAAEMEJwAAAEMEJwAAAEMEJwAAAEPM41QKZSdMyLPOfE8AAJQOjDgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYKpXBaePGjbLZbDp27Ji3SwEAAPAo0uDUo0cPjRs37pLb6dy5szIzM+V0Oi+9KAAAgCJSoiNOlmUpJyfnb4+rVKmSIiIiZLPZSqAqAAAAM0UWnOLi4rRp0ybNnTtXNptNNptNS5culc1m0wcffKC2bdvKbrdr2bJl8vHx0a5du/KcP3/+fEVHR8uyrPNu1S1dulRhYWH64IMP1KhRIzkcDvXr10+ZmZme83NycjR27FiFhYUpPDxcjzzyiEaMGKHrrruuqLoIAAAquCILTnPnzlWnTp00atQoZWZmKjMzU1FRUZKkSZMmKTExUfv27dO1116r3r17a8mSJXnOX7JkieLi4gocZTp16pSeffZZLVu2TJ988onS09M1ceJEz/6nn35ay5cv15IlS7R161a5XC6tWrXqgjW73W65XK48CwAAQEGKLDg5nU5VqlRJQUFBioiIUEREhHx9fSVJM2bMUJ8+fVSnTh2Fh4frrrvu0muvvSa32y1J+uKLL5SSkqI77rijwPazs7P14osvqm3btmrdurXGjBmj9evXe/bPnz9f8fHxuv7669WwYUM9//zzCgsLu2DNiYmJcjqdnuVc0AMAAMhPiTzj1LZt2zzr1113nfz8/PT2229LkhYvXqwrr7xSMTExBbYRFBSkOnXqeNYjIyN1+PBhSVJWVpZ+/vlntW/f3rPf19dXbdq0uWBd8fHxysrK8iwZGRmF7RoAAKhASiQ4BQcH51mvVKmShg0bpiVLlujMmTNasWKF7rzzzgu24e/vn2fdZrPJsqzztv3ZX/f/ld1uV2hoaJ4FAACgIEUanCpVqqSzZ88aHXvXXXfpo48+0oIFC5Sdna3Bgwdf9HWdTqdq1Kihzz77zLPt7Nmz+vzzzy+6TQAAgL/yK8rGYmJitGPHDh08eFAOh0O5ubkFHtuoUSN17NhRjzzyiO68804FBgZe0rUfeOABJSYmqm7dumrYsKHmz5+vo0ePMqUBAAAoMkU64jRx4kT5+vqqcePGqlatmtLT0y94/MiRI3XmzJm/vU1n4pFHHtGtt96q4cOHq1OnTnI4HOrbt68CAgIuuW0AAABJsll/9yBQMXriiSf0+uuva+/evUXedm5urho1aqSbb75Zjz/+uNE5LpdLTqdTWVlZXn3eKTthQp51/2lJXqoEAIDyrzC//4v0Vp2pEydOaN++fZo/f75xqPk7P/zwgz788EN1795dbrdbzz//vL7//nsNHTq0SNoHAADwypf8jhkzRl27dlX37t2L5DadJPn4+Gjp0qVq166dunTpor179+qjjz5So0aNiqR9AAAAr4w4LV26VEuXLi3SNqOiorR169YibRMAAODPvDLiBAAAUBYRnAAAAAwRnAAAAAwRnAAAAAwRnAAAAAwRnAAAAAx5ZToCXBgzhQMAUDox4gQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI6QhKoeyECUbHMW0BAAAlixEnAAAAQwQnAAAAQwQnAAAAQwQnAAAAQwQnAAAAQwQnAAAAQwQnAAAAQwQnAAAAQwQnAAAAQwQnAAAAQ2U2OPXo0UPjxo3zdhkAAKACKbPBCQAAoKSVyeAUFxenTZs2ae7cubLZbLLZbDp48KA2bdqk9u3by263KzIyUpMnT1ZOTo63ywUAAOVEmQxOc+fOVadOnTRq1ChlZmYqMzNT/v7+GjBggNq1a6cvvvhCL7zwgl5++WXNnDmzwHbcbrdcLleeBQAAoCB+3i7gYjidTlWqVElBQUGKiIiQJE2ZMkVRUVF6/vnnZbPZ1LBhQx06dEiPPPKIHnvsMfn4nJ8RExMTlZCQUNLlAwCAMqpMjjjlZ9++ferUqZNsNptnW5cuXXTixAn9+OOP+Z4THx+vrKwsz5KRkVFS5QIAgDKoTI445ceyrDyh6dw2SedtP8dut8tutxd7bQAAoHwosyNOlSpV0tmzZz3rjRs31rZt2zxhSZK2bdumkJAQ1apVyxslAgCAcqbMBqeYmBjt2LFDBw8e1JEjR3TfffcpIyNDDzzwgL7++mv997//1bRp0zR+/Ph8n28CAAAorDKbKCZOnChfX181btxY1apVU3Z2ttauXavPPvtMLVq00OjRozVy5EhNnTrV26UCAIByosw+41S/fn1t3749z7aYmBh99tlnXqoIAACUd2V2xAkAAKCkEZwAAAAMEZwAAAAMEZwAAAAMEZwAAAAMEZwAAAAMEZwAAAAMEZwAAAAMEZwAAAAMldmZw8sz/2lJ3i4BAADkgxEnAAAAQwQnAAAAQwQnAAAAQwQnAAAAQwQnAAAAQwQnAAAAQwSnUig7YYK3SwAAAPkgOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgq8eDUo0cPjRkzRmPGjFFYWJjCw8M1depUWZYlSfrXv/6ltm3bKiQkRBERERo6dKgOHz7sOf/o0aO67bbbVK1aNQUGBqpevXpasmSJJOnMmTMaM2aMIiMjFRAQoJiYGCUmJpZ0FwEAQDnl542LvvLKKxo5cqR27NihXbt26e6771Z0dLRGjRqlM2fO6PHHH1eDBg10+PBhPfTQQ4qLi9PatWslSf/v//0/paam6r333lPVqlX17bff6vTp05KkefPmafXq1frPf/6j2rVrKyMjQxkZGd7oIgAAKIe8EpyioqI0e/Zs2Ww2NWjQQHv37tXs2bM1atQo3XnnnZ7jYmNjNW/ePLVv314nTpyQw+FQenq6WrVqpbZt20qSYmJiPMenp6erXr166tq1q2w2m6Kjoy9Yh9vtltvt9qy7XK6i7SgAAChXvPKMU8eOHWWz2TzrnTp10oEDB3T27Fl9/vnnGjRokKKjoxUSEqIePXpI+iMUSdK9996r119/XS1bttSkSZO0bds2TztxcXFKSUlRgwYNNHbsWH344YcXrCMxMVFOp9OzREVFFX1nAQBAuVGqHg7//fffddVVV8nhcOhf//qXdu7cqbffflvSH88vSVL//v31ww8/aNy4cTp06JB69eqliRMnSpJat26t77//Xo8//rhOnz6tm2++WTfeeGOB14uPj1dWVpZn4bYeAAC4EK/cqvv000/PW69Xr56+/vprHTlyRE899ZRn9GfXrl3nnV+tWjXFxcUpLi5OV1xxhR5++GE9++yzkqTQ0FANGTJEQ4YM0Y033qh+/frpt99+U5UqVc5rx263y263F0MPAQBAeeSV4JSRkaHx48frnnvu0e7duzV//nwlJSWpdu3aqlSpkubPn6/Ro0fryy+/1OOPP57n3Mcee0xt2rRRkyZN5Ha7tWbNGjVq1EiSNHv2bEVGRqply5by8fHRG2+8oYiICIWFhXmhlwAAoLzxSnAaPny4Tp8+rfbt28vX11cPPPCA7r77btlsNi1dulSPPvqo5s2bp9atW+vZZ5/Vtdde6zm3UqVKio+P18GDBxUYGKgrrrhCr7/+uiTJ4XDo6aef1oEDB+Tr66t27dpp7dq18vEpVXckAQBAGWWzzk2gVEJ69Oihli1bas6cOSV5WSMul0tOp1NZWVkKDQ31Wh3ZCRPkPy3Ja9cHAKAiKczvf4ZiAAAADBGcAAAADJX4M04bN24s6UsCAAAUCUacAAAADBGcAAAADBGcAAAADBGcAAAADBGcAAAADBGcAAAADBGcSiFmDQcAoHQiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOJVC2QkTvF0CAADIB8EJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAUJkMTu+//766du2qsLAwhYeHa+DAgUpLS5MknTlzRmPGjFFkZKQCAgIUExOjxMREL1cMAADKgzIZnE6ePKnx48dr586dWr9+vXx8fHT99dcrNzdX8+bN0+rVq/Wf//xH+/fv17/+9S/FxMR4u2QAAFAO+Hm7gItxww035Fl/+eWXVb16daWmpio9PV316tVT165dZbPZFB0dXWA7brdbbrfbs+5yuYqtZgAAUPaVyRGntLQ0DR06VLGxsQoNDdXll18uSUpPT1dcXJxSUlLUoEEDjR07Vh9++GGB7SQmJsrpdHqWqKiokuoCAAAog8pkcLrmmmv066+/atGiRdqxY4d27Ngh6Y/nm1q3bq3vv/9ejz/+uE6fPq2bb75ZN954Y77txMfHKysry7NkZGSUZDcAAEAZU+Zu1f3666/at2+fFi5cqCuuuEKStGXLljzHhIaGasiQIRoyZIhuvPFG9evXT7/99puqVKmS5zi73S673V5itQMAgLKtzAWnypUrKzw8XC+99JIiIyOVnp6uyZMne/bPnj1bkZGRatmypXx8fPTGG28oIiJCYWFh3isaAACUC2UuOPn4+Oj111/X2LFj1bRpUzVo0EDz5s1Tjx49JEkOh0NPP/20Dhw4IF9fX7Vr105r166Vj0+ZvCsJAABKkTIXnCSpd+/eSk1NzbPNsizPz6NGjSrpkgAAQAXAMAwAAIAhghMAAIAhghMAAIAhghMAAIAhghMAAIAhghMAAIAhghMAAIAhghMAAIAhghMAAIAhglMp5D8tydslAACAfBCcAAAADBGcAAAADBGcAAAADBGcAAAADBGcAAAADBGcAAAADBGcAAAADPl5uwCcLzthQqHPYe4nAACKHyNOAAAAhghOAAAAhghOAAAAhghOAAAAhghOAAAAhghOAAAAhghOAAAAhghOAAAAhghOAAAAhgoVnHr06KFx48YVUykAAAClGyNOAAAAhghOAAAAhgodnHJzczVp0iRVqVJFERERmj59uiTp4MGDstlsSklJ8Rx77Ngx2Ww2bdy4UZK0ceNG2Ww2ffDBB2rVqpUCAwPVs2dPHT58WO+9954aNWqk0NBQ3XrrrTp16pSnnffff19du3ZVWFiYwsPDNXDgQKWlpXn2n7v2W2+9pSuvvFJBQUFq0aKFtm/ffnGvCgAAQD4KHZxeeeUVBQcHa8eOHZo1a5ZmzJihdevWFaqN6dOn6/nnn9e2bduUkZGhm2++WXPmzNGKFSv07rvvat26dZo/f77n+JMnT2r8+PHauXOn1q9fLx8fH11//fXKzc3N0+6UKVM0ceJEpaSkqH79+rr11luVk5NTYB1ut1sulyvPAgAAUBC/wp7QvHlzTZs2TZJUr149Pf/881q/fr3q1atn3MbMmTPVpUsXSdLIkSMVHx+vtLQ0xcbGSpJuvPFGbdiwQY888ogk6YYbbshz/ssvv6zq1asrNTVVTZs29WyfOHGirr76aklSQkKCmjRpom+//VYNGzbMt47ExEQlJCQY1w0AACq2Qo84NW/ePM96ZGSkDh8+fNFt1KhRQ0FBQZ7QdG7bn9tMS0vT0KFDFRsbq9DQUF1++eWSpPT09ALbjYyMlKQL1hYfH6+srCzPkpGRUah+AACAiqXQI07+/v551m02m3Jzc+Xj80cGsyzLsy87O/tv27DZbAW2ec4111yjqKgoLVq0SDVr1lRubq6aNm2qM2fOXLBdSefdzvszu90uu91e4H4AAIA/K7JP1VWrVk2SlJmZ6dn25wfFL9avv/6qffv2aerUqerVq5caNWqko0ePXnK7AAAAhVXoEaeCBAYGqmPHjnrqqacUExOjI0eOaOrUqZfcbuXKlRUeHq6XXnpJkZGRSk9P1+TJk4ugYgAAgMIp0nmcFi9erOzsbLVt21YPPvigZs6ceclt+vj46PXXX1dycrKaNm2qhx56SM8880wRVAsAAFA4NuvPDyVVcC6XS06nU1lZWQoNDfVaHdkJEwp9jv+0pGKoBACA8q8wv/+ZORwAAMAQwQkAAMAQwQkAAMAQwQkAAMAQwQkAAMAQwQkAAMAQwQkAAMAQwQkAAMBQkX3lCooOk1kCAFA6MeIEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiHmcSqHshAkXfS5zQAEAUHwYcQIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBULMGpR48eGjdu3EWfv3TpUoWFhRVZPQAAAEWhVI44DRkyRN988423ywAAAMijVH5XXWBgoAIDA71dBgAAQB7FNuKUk5OjMWPGKCwsTOHh4Zo6daosy5IknTlzRpMmTVKtWrUUHBysDh06aOPGjZ5z/3qrbvr06WrZsqWWLVummJgYOZ1O3XLLLTp+/LjnmOPHj+u2225TcHCwIiMjNXv27Eu+ZQgAAPBnxRacXnnlFfn5+WnHjh2aN2+eZs+erX/+85+SpDvuuENbt27V66+/rj179uimm25Sv379dODAgQLbS0tL06pVq7RmzRqtWbNGmzZt0lNPPeXZP378eG3dulWrV6/WunXrtHnzZu3evfuCNbrdbrlcrjwLAABAQYrtVl1UVJRmz54tm82mBg0aaO/evZo9e7Z69uyp1157TT/++KNq1qwpSZo4caLef/99LVmyRE8++WS+7eXm5mrp0qUKCQmRJA0bNkzr16/XE088oePHj+uVV17RihUr1KtXL0nSkiVLPO0XJDExUQkJCUXYawAAUJ4V24hTx44dZbPZPOudOnXSgQMHtGvXLlmWpfr168vhcHiWTZs2KS0trcD2YmJiPKFJkiIjI3X48GFJ0nfffafs7Gy1b9/es9/pdKpBgwYXrDE+Pl5ZWVmeJSMj42K7CwAAKgCvPBzu6+ur5ORk+fr65tnucDgKPMff3z/Pus1mU25uriR5np36c1D78/aC2O122e1247oBAEDFVmzB6dNPPz1vvV69emrVqpXOnj2rw4cP64orriiSa9WpU0f+/v767LPPFBUVJUlyuVw6cOCAunfvXiTXAAAAKLbglJGRofHjx+uee+7R7t27NX/+fCUlJal+/fq67bbbNHz4cCUlJalVq1Y6cuSIPv74YzVr1kwDBgwo9LVCQkI0YsQIPfzww6pSpYqqV6+uadOmycfH57xRKAAAgItVbMFp+PDhOn36tNq3by9fX1898MADuvvuuyX98eD2zJkzNWHCBP3vf/9TeHi4OnXqdFGh6ZznnntOo0eP1sCBAxUaGqpJkyYpIyNDAQEBRdUlAABQwdmsv3sQqIw6efKkatWqpaSkJI0cOdLoHJfLJafTqaysLIWGhhZzhQXLTphw0ef6T0sqwkoAACj/CvP7v1TOHH4xPv/8c3399ddq3769srKyNGPGDEnSoEGDvFwZAAAoL8pNcJKkZ599Vvv371elSpXUpk0bbd68WVWrVvV2WQAAoJwoN8GpVatWSk5O9nYZAACgHCu2CTABAADKG4ITAACAIYITAACAIYITAACAIYITAACAIYITAACAoXIzHUF5wuzfAACUTow4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI4lSLZCROUnTDB22UAAIACEJwAAAAMEZwAAAAMEZwAAAAMEZwAAAAMEZwAAAAMEZwAAAAMEZwAAAAMEZwAAAAMEZwAAAAMlarg1KNHD40bN87bZQAAAOSrVAUnAACA0ozgBAAAYKjUBafc3FxNmjRJVapUUUREhKZPn+7Z99xzz6lZs2YKDg5WVFSU7rvvPp04ccKz/4cfftA111yjypUrKzg4WE2aNNHatWu90AsAAFAelbrg9Morryg4OFg7duzQrFmzNGPGDK1bt06S5OPjo3nz5unLL7/UK6+8oo8//liTJk3ynHv//ffL7Xbrk08+0d69e/X000/L4XB4qysAAKCcsVmWZXm7iHN69Oihs2fPavPmzZ5t7du3V8+ePfXUU0+dd/wbb7yhe++9V0eOHJEkNW/eXDfccIOmTZtmdD232y232+1Zd7lcioqKUlZWlkJDQy+xN4WXnTBBkuQ/LanErw0AQEXlcrnkdDqNfv+XuhGn5s2b51mPjIzU4cOHJUkbNmxQnz59VKtWLYWEhGj48OH69ddfdfLkSUnS2LFjNXPmTHXp0kXTpk3Tnj17LnitxMREOZ1OzxIVFVU8nQIAAOVCqQtO/v7+edZtNptyc3P1ww8/aMCAAWratKlWrlyp5ORk/eMf/5AkZWdnS5Luuusufffddxo2bJj27t2rtm3bav78+QVeKz4+XllZWZ4lIyOj+DoGAADKvFIXnAqya9cu5eTkKCkpSR07dlT9+vV16NCh846LiorS6NGj9dZbb2nChAlatGhRgW3a7XaFhobmWQAAAAri5+0CTNWpU0c5OTmaP3++rrnmGm3dulUvvvhinmPGjRun/v37q379+jp69Kg+/vhjNWrUyEsVAwCA8qbMjDi1bNlSzz33nJ5++mk1bdpUy5cvV2JiYp5jzp49q/vvv1+NGjVSv3791KBBAy1YsMBLFQMAgPKmVH2qztsK81R9ceBTdQAAlLwy/ak6AACA0orgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYKjMfFddRcCM4QAAlG6MOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABhiHqdSKDthgtFxzPsEAEDJYsQJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAUJkJTj169NC4ceO8XQYAAKjAykxwAgAA8DaCEwAAgKEyFZxycnI0ZswYhYWFKTw8XFOnTpVlWZKkM2fOaNKkSapVq5aCg4PVoUMHbdy40bsFAwCAcqVMBadXXnlFfn5+2rFjh+bNm6fZs2frn//8pyTpjjvu0NatW/X6669rz549uummm9SvXz8dOHDAy1UDAIDyws/bBRRGVFSUZs+eLZvNpgYNGmjv3r2aPXu2evbsqddee00//vijatasKUmaOHGi3n//fS1ZskRPPvlkvu253W653W7PusvlKpF+AACAsqlMjTh17NhRNpvNs96pUycdOHBAu3btkmVZql+/vhwOh2fZtGmT0tLSCmwvMTFRTqfTs0RFRZVENwAAQBlVpkacLsTX11fJycny9fXNs93hcBR4Tnx8vMaPH+9Zd7lchCcAAFCgMhWcPv300/PW69Wrp1atWuns2bM6fPiwrrjiCuP27Ha77HZ7UZcJAADKqTJ1qy4jI0Pjx4/X/v379dprr2n+/Pl68MEHVb9+fd12220aPny43nrrLX3//ffauXOnnn76aa1du9bbZQMAgHKiTI04DR8+XKdPn1b79u3l6+urBx54QHfffbckacmSJZo5c6YmTJig//3vfwoPD1enTp00YMAAL1cNAADKC5t1biIkyOVyyel0KisrS6GhoV6rIzthgtFx/tOSirkSAADKv8L8/i9Tt+oAAAC8ieAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgqEx95UpFwYzgAACUTow4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGGIep1IoO2HCJbfBXFAAABQ9RpwAAAAMEZwAAAAMEZwAAAAMEZwAAAAMEZwAAAAMEZwAAAAMEZwAAAAMEZwAAAAMEZwAAAAMEZwAAAAMEZwAAAAMEZwAAAAMlZrg5Ha7NXbsWFWvXl0BAQHq2rWrdu7cKUnauHGjbDab1q9fr7Zt2yooKEidO3fW/v3787TxzjvvqE2bNgoICFBsbKwSEhKUk5Pjje4AAIByqNQEp0mTJmnlypV65ZVXtHv3btWtW1d9+/bVb7/95jlmypQpSkpK0q5du+Tn56c777zTs++DDz7Q7bffrrFjxyo1NVULFy7U0qVL9cQTTxR4TbfbLZfLlWcBAAAoiM2yLMvbRZw8eVKVK1fW0qVLNXToUElSdna2YmJiNG7cOLVr105XXnmlPvroI/Xq1UuStHbtWl199dU6ffq0AgIC1K1bN/Xv31/x8fGedv/1r39p0qRJOnToUL7XnT59uhISEs7bnpWVpdDQ0GLoqZnshAmX3Ib/tKQiqAQAgPLP5XLJ6XQa/f4vFSNOaWlpys7OVpcuXTzb/P391b59e+3bt8+zrXnz5p6fIyMjJUmHDx+WJCUnJ2vGjBlyOByeZdSoUcrMzNSpU6fyvW58fLyysrI8S0ZGRnF0DwAAlBN+3i5Aks4NetlstvO2/3mbv7+/5+dz23Nzcz3/TUhI0ODBg89rPyAgIN/r2u122e32SyseAABUGKVixKlu3bqqVKmStmzZ4tmWnZ2tXbt2qVGjRkZttG7dWvv371fdunXPW3x8SkU3AQBAGVcqRpyCg4N177336uGHH1aVKlVUu3ZtzZo1S6dOndLIkSP1xRdf/G0bjz32mAYOHKioqCjddNNN8vHx0Z49e7R3717NnDmzBHoBAADKu1IRnCTpqaeeUm5uroYNG6bjx4+rbdu2+uCDD1S5cmWj8/v27as1a9ZoxowZmjVrlvz9/dWwYUPdddddxVw5AACoKErFp+pKi8I8VV+c+FQdAAAlp8x9qg4AAKAsIDgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYKjUzh+P/MHklAAClEyNOAAAAhghOAAAAhghOAAAAhghOAAAAhghOAAAAhghOAAAAhghOAAAAhpjHqRTKTphQZG0xJxQAAEWHEScAAABDBCcAAABDBCcAAABDBCcAAABDBCcAAABDBCcAAABDBCcAAABDBCcAAABDBCcAAABD5To4bd26Vc2aNZO/v7+uu+46b5cDAADKuHL9lSvjx49Xy5Yt9d5778nhcHi7HAAAUMaV6xGntLQ09ezZU5dddpnCwsK8XQ4AACjjynRwcrvdGjt2rKpXr66AgAB17dpVO3fu1MGDB2Wz2fTrr7/qzjvvlM1m09KlS71dLgAAKOPKdHCaNGmSVq5cqVdeeUW7d+9W3bp11bdvX4WEhCgzM1OhoaGaM2eOMjMzNWTIkPPOd7vdcrlceRYAAICClNngdPLkSb3wwgt65pln1L9/fzVu3FiLFi1SYGCgFi9erIiICNlsNjmdTkVERCgwMPC8NhITE+V0Oj1LVFSUF3oCAADKijIbnNLS0pSdna0uXbp4tvn7+6t9+/bat2+fURvx8fHKysryLBkZGcVVLgAAKAfK7KfqLMuSJNlstvO2/3VbQex2u+x2e5HXBgAAyqcyO+JUt25dVapUSVu2bPFsy87O1q5du9SoUSMvVgYAAMqrMjviFBwcrHvvvVcPP/ywqlSpotq1a2vWrFk6deqURo4c6e3yAABAOVRmg5MkPfXUU8rNzdWwYcN0/PhxtW3bVh988IEqV67s7dIAAEA5VKaDU0BAgObNm6d58+blu//YsWMlWxAAACjXyuwzTgAAACWN4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCoTM8cXl75T0vydgkAACAfjDgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYYjqCUig7YYK3SwAAoNQpDdP1MOIEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgqFwHJ5vNplWrVnm7DAAAUE6U6+AEAABQlAhOAAAAhkpVcHrzzTfVrFkzBQYGKjw8XL1799bJkye1c+dO9enTR1WrVpXT6VT37t21e/fuPOceOHBA3bp1U0BAgBo3bqx169Z5qRcAAKC88vN2AedkZmbq1ltv1axZs3T99dfr+PHj2rx5syzL0vHjxzVixAjNmzdPkpSUlKQBAwbowIEDCgkJUW5urgYPHqyqVavq008/lcvl0rhx4/72mm63W26327PucrmKq3sAAKAcKFXBKScnR4MHD1Z0dLQkqVmzZpKknj175jl24cKFqly5sjZt2qSBAwfqo48+0r59+3Tw4EFddtllkqQnn3xS/fv3v+A1ExMTlZCQUAy9AQAA5VGpuVXXokUL9erVS82aNdNNN92kRYsW6ejRo5Kkw4cPa/To0apfv76cTqecTqdOnDih9PR0SdK+fftUu3ZtT2iSpE6dOv3tNePj45WVleVZMjIyiqdzAACgXCg1wcnX11fr1q3Te++9p8aNG2v+/Plq0KCBvv/+e8XFxSk5OVlz5szRtm3blJKSovDwcJ05c0aSZFnWee3ZbLa/vabdbldoaGieBQAAoCClJjhJf4SdLl26KCEhQZ9//rkqVaqkt99+W5s3b9bYsWM1YMAANWnSRHa7XUeOHPGc17hxY6Wnp+vQoUOebdu3b/dGFwAAQDlWap5x2rFjh9avX6+rrrpK1atX144dO/TLL7+oUaNGqlu3rpYtW6a2bdvK5XLp4YcfVmBgoOfc3r17q0GDBho+fLiSkpLkcrk0ZcoUL/YGAACUR6VmxCk0NFSffPKJBgwYoPr162vq1KlKSkpS//79tXjxYh09elStWrXSsGHDNHbsWFWvXt1zro+Pj95++2253W61b99ed911l5544gkv9gYAAJRHNiu/B4QqKJfLJafTqaysLK8+75SdMMFr1wYAoLTyn5ZULO0W5vd/qRlxAgAAKO0ITgAAAIYITgAAAIYITgAAAIYITgAAAIYITgAAAIYITgAAAIYITgAAAIYITgAAAIZKzXfV4f8U18yoAADg0jDiBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjpCEqh7IQJRdoe0xsAAFA0GHECAAAwRHACAAAwRHACAAAwRHACAAAwRHACAAAwRHACAAAwRHACAAAwRHACAAAwRHACAAAwVKqD08aNG2Wz2XTs2DFvlwIAAHBpwemXX36Rv7+/Tp06pZycHAUHBys9PT3fY9944w117txZkrR161bFxsZeyqUBAABK3CUFp+3bt6tly5YKCgpScnKyqlSpotq1axd4bJcuXSRJW7Zs8fwMAABQVlxScNq2bZtxGDI5du3atapfv74CAwN15ZVX6uDBg/m2061bNwUGBioqKkpjx47VyZMnPfsXLFigevXqKSAgQDVq1NCNN954KV0EAADwsFmWZRXmhPT0dDVv3lySdOrUKfn6+sput+v06dOy2WwKCAjQ0KFDtWDBAq1YsUL33XefJMnlcik4OFi+vr46fvy4AgMD5efnpwULFmjo0KHKyMhQvXr1NHr0aN17773atWuXJkyYoJ9//llHjx5VWFiY9u7dq86dO+vxxx/X1VdfrV9++UVjxoxRixYttGTJEu3atUsdO3bUsmXL1LlzZ/3222/avHmzxo4da9Q3l8slp9OprKwshYaGFvKlLDrZCROKtD3/aUlF2h4AAOVJYX7/Fzo45eTk6Mcff5TL5VLbtm21c+dOORwOtWzZUu+++65q164th8OhqlWr6sSJEzpy5Ig++ugjzZkzR2vWrNGePXs0evRobdu2TZJUtWpVORwOPfroo1q1apW++uor2Ww2SdLkyZP19NNPe4LT8OHDFRgYqIULF3rq2bJli7p3766TJ09q7dq1uuOOO/Tjjz8qJCTkb/vidrvldrvzvHBRUVEEJwAAKpDCBKdC36rz8/NTTEyMvv76a7Vr104tWrTQTz/9pBo1aqhbt26KiYlR1apVJUkOh0MxMTHavXu3Bg0apJiYGO3du1cDBgxQTEyMYmJi5HA4JEn79u1Tx44dPaFJkjp16pTn2snJyVq6dKkcDodn6du3r3Jzc/X999+rT58+io6OVmxsrIYNG6bly5fr1KlTBfYlMTFRTqfTs0RFRRX25QAAABWIX2FPaNKkiX744QdlZ2crNzdXDodDOTk5ysnJkcPhUHR0tL766iulp6ercePGkqTff/9dfn5+mjt3rtxut3x8fPT666/r9ttv14svvihJMhn4ys3N1T333JPvrbfatWurUqVK2r17tzZu3KgPP/xQjz32mKZPn66dO3cqLCzsvHPi4+M1fvx4z/q5EScAAID8FDo4rV27VtnZ2erVq5dmzZqlNm3a6JZbblFcXJz69esnf39/SVLNmjWVkpKin3/+Wb169VJKSorOnj2rli1bavPmzapSpUqe4bDGjRtr1apVea716aef5llv3bq1vvrqK9WtW7fgDvn5qXfv3urdu7emTZumsLAwffzxxxo8ePB5x9rtdtnt9sK+BAAAoIIq9K266OhoORwO/fzzzxo0aJBq166t1NRUDR48WHXr1lV0dLSkPwJM3bp1lZGRoQ4dOqhhw4b69ddfFRsbq/bt26tu3bqqXr26p93Ro0crLS1N48eP1/79+7VixQotXbo0z7UfeeQRbd++Xffff79SUlJ04MABrV69Wg888IAkac2aNZo3b55SUlL0ww8/6NVXX1Vubq4aNGhwCS8RAADAHy5qOoKNGzeqXbt2CggI0I4dO1SrVi3VrFmzwGO7desmSdq0aZPn57+qXbu2Vq5cqXfeeUctWrTQiy++qCeffDLPMc2bN9emTZt04MABXXHFFWrVqpX+3//7f4qMjJQkhYWF6a233lLPnj3VqFEjvfjii3rttdfUpEmTi+kmAABAHoX+VF15xnQEAABUPMX6qToAAICKiuAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgqNBf8ovix0zfAACUTow4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGGIep1IoO2GCt0sAyhzmPwNQEhhxAgAAMERwAgAAMERwAgAAMERwAgAAMERwAgAAMERwAgAAMERwAgAAMERwAgAAMERwAgAAMFRqgtMvv/wif39/nTp1Sjk5OQoODlZ6enq+x77xxhvq3LmzJGnr1q2KjY3Ns3/jxo2y2Ww6duxYcZcNAAAqkFITnLZv366WLVsqKChIycnJqlKlimrXrl3gsV26dJEkbdmyxfMzAABAcSo1wWnbtm3GYehCxx48eFBXXnmlJKly5cqy2WyKi4srvsIBAECFYbMsy/LWxdPT09W8eXNJ0qlTp+Tr6yu73a7Tp0/LZrMpICBAQ4cO1YIFC7RixQrdd999kiSXy6Xg4GD5+vrq+PHjCgwMlJ+fnxYsWKAhQ4bov//9r2644Qbt379foaGhCgwMlNPp/Nt6XC6XnE6nsrKyFBoaWqx9vxC+5BcoPL7kF8DFKszvf78SqilfNWvWVEpKilwul9q2batPP/1UDodDLVu21LvvvqvatWvL4XBIkq699lp17txZH330kebMmaM1a9Zoz549Gj16tLZt2yZJqlq1qnx9fVWlShVJUvXq1RUWFlbg9d1ut9xut2fd5XIVX2cBAECZ59VbdX5+foqJidHXX3+tdu3aqUWLFvrpp59Uo0YNdevWTTExMapataokyeFwKCYmRrt379agQYMUExOjvXv3asCAAYqJiVFMTIwnZJlKTEyU0+n0LFFRUcXRTQAAUE549VZdkyZN9MMPPyg7O1u5ubmy2+3KyclRTk6OAgICFB0dra+++krp6elq3LixJOn333+Xn5+f/Pz85Ha75ePjI39/f91+++168cUXJf3xqborr7xSR48eLfSIU1RUFLfqgDKIW3UALlaZuVW3du1aZWdnq1evXpo1a5batGmjW265RXFxcerXr5/8/f0l/d8tvZ9//lm9evVSSkqKzp49q5YtW2rz5s2qUqVKno5WqlRJknT27NkLXt9ut8tutxdfBwEAQLni1Vt10dHRcjgc+vnnnzVo0CDVrl1bqampGjx4sOrWravo6GhJf9zSq1u3rjIyMtShQwc1bNhQv/76q2JjY9W+fXvVrVtX1atXz9OuzWbTmjVr9Msvv+jEiRPe6iIAAChHvD4dwcaNG9WuXTsFBARox44dqlWrlmrWrFngsd26dZMkbdq0yfPzX9WqVUsJCQmaPHmyatSooTFjxhRb/QAAoOLw6jNOpQ3TEQBlF884AbhYhfn97/URJwAAgLKC4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGDIq1/yi/wxAzIAAKUTI04AAACGCE4AAACGCE4AAACGCE4AAACGCE4AAACGCE4AAACGCE4AAACGmMepFMpOmFBi12LOKAAAzDHiBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYKhYgtPGjRtls9l07NixAo+ZPn26WrZsWRyXBwAAKBZFEpx69OihcePGFeqciRMnav369UVxeQAAgBLhte+qczgccjgc3ro8AABAoV3yiFNcXJw2bdqkuXPnymazyWaz6eDBg5Kk5ORktW3bVkFBQercubP279/vOe+vt+ri4uJ03XXX6dlnn1VkZKTCw8N1//33Kzs723NMZmamrr76agUGBuryyy/XihUrFBMTozlz5uRpt3bt2rLb7apZs6bGjh17qV0EAACQVAQjTnPnztU333yjpk2basaMGZKkr776SpI0ZcoUJSUlqVq1aho9erTuvPNObd26tcC2NmzYoMjISG3YsEHffvuthgwZopYtW2rUqFGSpOHDh+vIkSPauHGj/P39NX78eB0+fNhz/ptvvqnZs2fr9ddfV5MmTfTTTz/piy++KPB6brdbbrfbs+5yuS7ptQAAAOXbJQcnp9OpSpUqKSgoSBEREZKkr7/+WpL0xBNPqHv37pKkyZMn6+qrr9bvv/+ugICAfNuqXLmynn/+efn6+qphw4a6+uqrtX79eo0aNUpff/21PvroI+3cuVNt27aVJP3zn/9UvXr1POenp6crIiJCvXv3lr+/v2rXrq327dsXWHtiYqISEhIu9SUAAAAVRLFOR9C8eXPPz5GRkZKUZ4Tor5o0aSJfX98855w7fv/+/fLz81Pr1q09++vWravKlSt71m+66SadPn1asbGxGjVqlN5++23l5OQUeL34+HhlZWV5loyMjMJ3EgAAVBjFGpz8/f09P9tsNklSbm6u0fHnzjl3vGVZ+Z7z5+1RUVHav3+//vGPfygwMFD33XefunXrluc5qT+z2+0KDQ3NswAAABSkSIJTpUqVdPbs2aJoqkANGzZUTk6OPv/8c8+2b7/99ry5ogIDA3Xttddq3rx52rhxo7Zv3669e/cWa20AAKBiKJLpCGJiYrRjxw4dPHhQDofjgqNKF6thw4bq3bu37r77br3wwgvy9/fXhAkTFBgY6BnNWrp0qc6ePasOHTooKChIy5YtU2BgoKKjo4u8HgAAUPEUyYjTxIkT5evrq8aNG6tatWpKT08vimbP8+qrr6pGjRrq1q2brr/+eo0aNUohISGeh83DwsK0aNEidenSRc2bN9f69ev1zjvvKDw8vFjqAQAAFYvNKujhoTLgxx9/VFRUlD766CP16tXrkttzuVxyOp3Kysry6vNO2QkTSuxa/tOSSuxaAACURoX5/e+1mcMvxscff6wTJ06oWbNmyszM1KRJkxQTE6Nu3bp5uzQAAFABlKnglJ2drUcffVTfffedQkJC1LlzZy1fvvy8T+MBAAAUhzIVnPr27au+fft6uwwAAFBBFes8TgAAAOUJwQkAAMAQwQkAAMAQwQkAAMAQwQkAAMBQmfpUXUXBpJQAAJROjDgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYYh6nUig7YUKB+5jjCQAA72HECQAAwBDBCQAAwBDBCQAAwBDBCQAAwBDBCQAAwBDBCQAAwBDBCQAAwBDBCQAAwBDBCQAAwFC5D049evTQuHHjvF0GAAAoB8r9V6689dZb8vf393YZAACgHCj3walKlSreLgEAAJQT3KoDAAAwVO5HnC7E7XbL7XZ71l0ulxerAQAApV25H3G6kMTERDmdTs8SFRXl7ZIAAEApVqGDU3x8vLKysjxLRkaGt0sCAAClWIW+VWe322W3271dBgAAKCMq9IgTAABAYRCcAAAADBGcAAAADJX7Z5w2btzo7RIAAEA5wYgTAACAIYITAACAIYITAACAIYITAACAIYITAACAIYITAACAIYITAACAIYITAACAIYITAACAoXI/c3hZ5D8tydslAACAfDDiBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjpCEqB7IQJedaZjgAAgNKJEScAAABDBCcAAABDBCcAAABDBCcAAABDBCcAAABDBCcAAABDBCcAAABDBCcAAABDBCcAAABDBCcAAABD5T44xcXFafr06d4uAwAAlAPlPjgBAAAUFYITAACAIYITAACAIT9vF1Dcli5dWuA+t9stt9vtWXe5XCVQEQAAKKsq9IhTYmKinE6nZ4mKivJ2SQAAoBSr0MEpPj5eWVlZniUjI8PbJQEAgFKs3N+quxC73S673e7tMgAAQBlRoUecAAAACoPgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYKhCf8lvaeE/LcnbJQAAAAOMOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABgiOAEAABhiHqdSKDthwnnbmOsJAADvY8QJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAUJkJTgsXLlStWrWUm5ubZ/u1116rESNGSJLeeecdtWnTRgEBAYqNjVVCQoJycnK8US4AACiHykxwuummm3TkyBFt2LDBs+3o0aP64IMPdNttt+mDDz7Q7bffrrFjxyo1NVULFy7U0qVL9cQTT3ixagAAUJ6UmeBUpUoV9evXTytWrPBse+ONN1SlShX16tVLTzzxhCZPnqwRI0YoNjZWffr00eOPP66FCxcW2Kbb7ZbL5cqzAAAAFKTMBCdJuu2227Ry5Uq53W5J0vLly3XLLbfI19dXycnJmjFjhhwOh2cZNWqUMjMzderUqXzbS0xMlNPp9CxRUVEl2R0AAFDG+Hm7gMK45pprlJubq3fffVft2rXT5s2b9dxzz0mScnNzlZCQoMGDB593XkBAQL7txcfHa/z48Z51l8tFeAIAAAUqU8EpMDBQgwcP1vLly/Xtt9+qfv36atOmjSSpdevW2r9/v+rWrWvcnt1ul91uL65yAQBAOVOmgpP0x+26a665Rl999ZVuv/12z/bHHntMAwcOVFRUlG666Sb5+Phoz5492rt3r2bOnOnFigEAQHlRpp5xkqSePXuqSpUq2r9/v4YOHerZ3rdvX61Zs0br1q1Tu3bt1LFjRz333HOKjo72YrUAAKA8KXMjTr6+vjp06FC++/r27au+ffuWcEUAAKCiKHMjTgAAAN5CcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBEcAIAADBU5mYOrwj8pyV5uwQAAJAPRpwAAAAMEZwAAAAMEZwAAAAMEZwAAAAMEZwAAAAMEZwAAAAMEZwAAAAMMY9TKZSdMOGiz2UOKAAAig8jTgAAAIYITgAAAIYITgAAAIYITgAAAIYITgAAAIYITgAAAIYITgAAAIYITgAAAIYITgAAAIYITgAAAIYITgAAAIYqRHDKzs72dgkAAKAc8EpwsixLs2bNUmxsrAIDA9WiRQu9+eabys3N1WWXXaYXX3wxz/G7d++WzWbTd999J0nKysrS3XffrerVqys0NFQ9e/bUF1984Tl++vTpatmypRYvXqzY2FjZ7XZZllWifQQAAOWPV4LT1KlTtWTJEr3wwgv66quv9NBDD+n222/X5s2bdcstt2j58uV5jl+xYoU6deqk2NhYWZalq6++Wj/99JPWrl2r5ORktW7dWr169dJvv/3mOefbb7/Vf/7zH61cuVIpKSn51uF2u+VyufIsAAAABfEr6QuePHlSzz33nD7++GN16tRJkhQbG6stW7Zo4cKFevjhh/Xcc8/phx9+UHR0tHJzc/X666/r0UcflSRt2LBBe/fu1eHDh2W32yVJzz77rFatWqU333xTd999tyTpzJkzWrZsmapVq1ZgLYmJiUpISCjmHgMAgPKixEecUlNT9fvvv6tPnz5yOBye5dVXX1VaWppatWqlhg0b6rXXXpMkbdq0SYcPH9bNN98sSUpOTtaJEycUHh6e5/zvv/9eaWlpnutER0dfMDRJUnx8vLKysjxLRkZG8XUcAACUeSU+4pSbmytJevfdd1WrVq08+86NIN12221asWKFJk+erBUrVqhv376qWrWq5/zIyEht3LjxvLbDwsI8PwcHB/9tLXa73XNNAACAv1Piwalx48ay2+1KT09X9+7d8z1m6NChmjp1qpKTk/Xmm2/qhRde8Oxr3bq1fvrpJ/n5+SkmJqaEqgYAAPBCcAoJCdHEiRP10EMPKTc3V127dpXL5dK2bdvkcDg0YsQIXX755ercubNGjhypnJwcDRo0yHN+79691alTJ1133XV6+umn1aBBAx06dEhr167Vddddp7Zt25Z0lwAAQAVR4sFJkh5//HFVr15diYmJ+u677xQWFqbWrVt7HgCX/rhdd//992v48OEKDAz0bLfZbFq7dq2mTJmiO++8U7/88osiIiLUrVs31ahRwxvdAQAAFYTNYoIjD5fLJafTqaysLIWGhnqtjuyECRd9rv+0pCKsBACA8q8wv/8rxMzhAAAARYHgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYMgrM4fjwpjEEgCA0okRJwAAAEMEJwAAAEMEJwAAAEMEJwAAAEMEJwAAAEMEJwAAAEMEJwAAAEPM4+Rl2QkTztvGPE4AAJROjDgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYIjgBAAAYKtHg9Oabb6pZs2YKDAxUeHi4evfurZMnT0qSlixZokaNGikgIEANGzbUggUL8pz7v//9T0OGDFHlypUVHh6uQYMG6eDBg579cXFxuu666/Tss88qMjJS4eHhuv/++5WdnV2SXQQAAOWYX0ldKDMzU7feeqtmzZql66+/XsePH9fmzZtlWZYWLVqkadOm6fnnn1erVq30+eefa9SoUQoODtaIESN06tQpXXnllbriiiv0ySefyM/PTzNnzlS/fv20Z88eVapUSZK0YcMGRUZGasOGDfr22281ZMgQtWzZUqNGjcq3JrfbLbfb7Vl3uVwl8loAAICyqUSDU05OjgYPHqzo6GhJUrNmzSRJjz/+uJKSkjR48GBJ0uWXX67U1FQtXLhQI0aM0Ouvvy4fHx/985//lM1mk/THCFVYWJg2btyoq666SpJUuXJlPf/88/L19VXDhg119dVXa/369QUGp8TERCUkJBR31wEAQDlRYsGpRYsW6tWrl5o1a6a+ffvqqquu0o033qicnBxlZGRo5MiReQJOTk6OnE6nJCk5OVnffvutQkJC8rT5+++/Ky0tzbPepEkT+fr6etYjIyO1d+/eAmuKj4/X+PHjPesul0tRUVGX3FcAAFA+lVhw8vX11bp167Rt2zZ9+OGHmj9/vqZMmaJ33nlHkrRo0SJ16NDhvHMkKTc3V23atNHy5cvPa7datWqen/39/fPss9lsys3NLbAmu90uu91+0X0CAAAVS4kFJ+mPINOlSxd16dJFjz32mKKjo7V161bVqlVL3333nW677bZ8z2vdurX+/e9/q3r16goNDS3JkgEAADxK7FN1O3bs0JNPPqldu3YpPT1db731ln755Rc1atRI06dPV2JioubOnatvvvlGe/fu1ZIlS/Tcc89Jkm677TZVrVpVgwYN0ubNm/X9999r06ZNevDBB/Xjjz+WVBcAAEAFV2IjTqGhofrkk080Z84cuVwuRUdHKykpSf3795ckBQUF6ZlnntGkSZMUHBysZs2aady4cZ59n3zyiR555BENHjxYx48fV61atdSrVy9GoAAAQImxWZZlebuI0sLlcsnpdCorK6vEAll2woTztvlPSyqRawMAgML9/mfmcAAAAEMEJwAAAEMEJwAAAEMEJwAAAEMEJwAAAEMEJwAAAEMEJwAAAEMEJwAAAEMEJwAAAEMl+iW/OB+zhAMAUHYw4gQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI6QhKoeyECUXSDlMdAABQtBhxAgAAMERwAgAAMERwAgAAMERwAgAAMERwAgAAMERwAgAAMERwAgAAMERwAgAAMERwAgAAMERwAgAAMERwAgAAMERwAgAAMERwAgAAMERwAgAAMOTn7QK8ye12y+12e9ZdLpcXqwEAAKVdhR5xSkxMlNPp9CxRUVHeLgkAAJRiFTo4xcfHKysry7NkZGR4uyQAAFCKVehbdXa7XXa73dtlAACAMqLcjzg9//zz6tWrl7fLAAAA5UC5D05HjhxRWlqat8sAAADlQLkPTtOnT9fBgwe9XQYAACgHyn1wAgAAKCoEJwAAAEMEJwAAAEMEJwAAAEMEJwAAAEMEJwAAAEMEJwAAAEMEJwAAAEMEJwAAAEMV+kt+Syv/aUneLgEAAOSDEScAAABDBCcAAABDBCcAAABDBCcAAABDBCcAAABDBCcAAABDTEdQCmUnTPD8zNQEAACUHow4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCI4AQAAGCrW4NSjRw/ZbDbZbDalpKQU56XyOHjwoOe6LVu2LLHrAgCA8q3YR5xGjRqlzMxMNW3aVJK0cuVKdejQQU6nUyEhIWrSpIkmTPi/rxhZunSpJ/T8eQkICPAcExcX59nu7++v2NhYTZw4USdPnpQkRUVFKTMzM0+7AAAAl6rYv6suKChIERERkqSPPvpIt9xyi5588klde+21stlsSk1N1fr16/OcExoaqv379+fZZrPZ8qz369dPS5YsUXZ2tjZv3qy77rpLJ0+e1AsvvCBfX19FRETI4XAUb+cAAECFUqJf8rtmzRp17dpVDz/8sGdb/fr1dd111+U5zmazecJWQex2u+eYoUOHasOGDVq1apVeeOGFIq8bAABAKuGHwyMiIvTVV1/pyy+/LPK2AwMDlZ2dXeTtAgAAnFOiwemBBx5Qu3bt1KxZM8XExOiWW27R4sWL5Xa78xyXlZUlh8ORZ7nqqqsKbPezzz7TihUr1KtXr0LV43a75XK58iwAAAAFKdFbdcHBwXr33XeVlpamDRs26NNPP9WECRM0d+5cbd++XUFBQZKkkJAQ7d69O8+5gYGBedbXrFkjh8OhnJwcZWdna9CgQZo/f36h6klMTFRCQsKldQoAAFQYJRqczqlTp47q1Kmju+66S1OmTFH9+vX173//W3fccYckycfHR3Xr1r1gG1deeaVeeOEF+fv7q2bNmvL39y90HfHx8Ro/frxn3eVyKSoqqtDtAACAisErwenPYmJiFBQU5JlKwFRwcPDfhqu/Y7fbZbfbL6kNAABQcZRocJo+fbpOnTqlAQMGKDo6WseOHdO8efOUnZ2tPn36eI6zLEs//fTTeedXr15dPj5Mdg4AALyjRINT9+7d9Y9//EPDhw/Xzz//rMqVK6tVq1b68MMP1aBBA89xLpdLkZGR552fmZn5t9MUAAAAFJcSDU5XXnmlrrzyygseExcXp7i4uAses3Tp0qIrCgAAwFCx3/dasGCBHA6H9u7dW9yX8khPT5fD4dCTTz5ZYtcEAADlX7GOOC1fvlynT5+WJNWuXbs4L5VHzZo1PV8qzMPfAACgqBRrcKpVq1ZxNl8gPz+/S/7EHQAAwF/xETUAAABDBCcAAABDBCcAAABDBCcAAABDBCcAAABDBCcAAABDXv+SX5zPf1qSt0sAAAD5YMQJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAEMEJAADAkJ+3CyhNLMuSJLlcLi9XAgAASsq53/vncsCFEJz+5Pjx45KkqKgoL1cCAABK2vHjx+V0Oi94jM0yiVcVRG5urg4dOqSQkBDZbDav1OByuRQVFaWMjAyFhoZ6pQbkxXtSuvB+lD68J6UP70nhWJal48ePq2bNmvLxufBTTIw4/YmPj48uu+wyb5chSQoNDeUPeynDe1K68H6UPrwnpQ/vibm/G2k6h4fDAQAADBGcAAAADBGcShm73a5p06bJbrd7uxT8/3hPShfej9KH96T04T0pPjwcDgAAYIgRJwAAAEMEJwAAAEMEJwAAAEMEJwAAAEMEJy9YsGCBLr/8cgUEBKhNmzbavHnzBY/ftGmT2rRpo4CAAMXGxurFF18soUorjsK8J2+99Zb69OmjatWqKTQ0VJ06ddIHH3xQgtWWf4X9O3LO1q1b5efnp5YtWxZvgRVQYd8Tt9utKVOmKDo6Wna7XXXq1NHixYtLqNqKobDvyfLly9WiRQsFBQUpMjJSd9xxh3799dcSqrYcsVCiXn/9dcvf399atGiRlZqaaj344INWcHCw9cMPP+R7/HfffWcFBQVZDz74oJWammotWrTI8vf3t958880Srrz8Kux78uCDD1pPP/209dlnn1nffPONFR8fb/n7+1u7d+8u4crLp8K+H+ccO3bMio2Nta666iqrRYsWJVNsBXEx78m1115rdejQwVq3bp31/fffWzt27LC2bt1aglWXb4V9TzZv3mz5+PhYc+fOtb777jtr8+bNVpMmTazrrruuhCsv+whOJax9+/bW6NGj82xr2LChNXny5HyPnzRpktWwYcM82+655x6rY8eOxVZjRVPY9yQ/jRs3thISEoq6tArpYt+PIUOGWFOnTrWmTZtGcCpihX1P3nvvPcvpdFq//vprSZRXIRX2PXnmmWes2NjYPNvmzZtnXXbZZcVWY3nFrboSdObMGSUnJ+uqq67Ks/2qq67Stm3b8j1n+/bt5x3ft29f7dq1S9nZ2cVWa0VxMe/JX+Xm5ur48eOqUqVKcZRYoVzs+7FkyRKlpaVp2rRpxV1ihXMx78nq1avVtm1bzZo1S7Vq1VL9+vU1ceJEnT59uiRKLvcu5j3p3LmzfvzxR61du1aWZennn3/Wm2++qauvvrokSi5X+JLfEnTkyBGdPXtWNWrUyLO9Ro0a+umnn/I956effsr3+JycHB05ckSRkf9fO3cPklwbxgH8b56kEGooIkmoxaKGoA+MkhDCqaEpCoowqEEiooLAKZ4gaKqhqCapRSMaWkOXJMciITIo+hikICICSxqq6x0e8n17Ct5zxA8e/f/gDN7ewnX4czzXuT0eU9rqzQfJZPKnxcVFvLy8oK+vLx0l5pVk8ri4uIDb7cbBwQEUhV9pqZZMJldXVwiFQigqKsLu7i4eHh4wNjaGx8dH3ueUAslk0tHRAa/Xi/7+fry+vuLt7Q09PT1YWVnJRMk5hStOWaDT6b68FpFvY/83/6dxSp7WTD5tbW3h169f2N7eRkVFRbrKyztq83h/f8fAwADm5uZQW1ubqfLykpZj5OPjAzqdDl6vF1arFd3d3VhaWsLm5iZXnVJISyaRSAQTExOYnZ3F0dER9vb2cH19DZfLlYlScwovzzKovLwcer3+2xXB/f39tyuHT5WVlT/OVxQFZWVlaas1XySTyaft7W2MjIxgZ2cHDocjnWXmDa15xGIxHB4e4vj4GOPj4wB+n7RFBIqiwO/3o6urKyO156pkjhGTyYSqqiqUlpYmxurr6yEiiEajsFgsaa051yWTycLCAmw2G2ZmZgAAjY2NMBqN6OzsxPz8PH+90IArThlkMBjQ0tKCQCDwZTwQCKCjo+PHz7S3t3+b7/f70draisLCwrTVmi+SyQT4vdI0PDwMn8/HewRSSGseJSUlODk5QTgcTmwulwt1dXUIh8Noa2vLVOk5K5ljxGaz4fb2Fs/Pz4mx8/NzFBQUwGw2p7XefJBMJvF4HAUFX0/5er0ewL+/YpBK2borPV99/oXU4/FIJBKRyclJMRqNcnNzIyIibrdbhoaGEvM/H0cwNTUlkUhEPB4PH0eQYloz8fl8oiiKrK6uyt3dXWJ7enrK1i7kFK15/In/qks9rZnEYjExm83S29srp6enEgwGxWKxyOjoaLZ2IedozWRjY0MURZG1tTW5vLyUUCgkra2tYrVas7ULfy02Tlmwuroq1dXVYjAYpLm5WYLBYOI9p9Mpdrv9y/z9/X1pamoSg8EgNTU1sr6+nuGKc5+WTOx2uwD4tjmdzswXnqO0HiP/xcYpPbRmcnZ2Jg6HQ4qLi8VsNsv09LTE4/EMV53btGayvLwsDQ0NUlxcLCaTSQYHByUajWa46r+fToRrdERERERq8B4nIiIiIpXYOBERERGpxMaJiIiISCU2TkREREQqsXEiIiIiUomNExEREZFKbJyIiIiIVGLjRERERKQSGyciIiIildg4EREREanExomIiIhIJTZORERERCr9AzRBvZkn+Z1mAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved interpretation plots for sample 4: 'sadness'\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved interpretation plots for sample 5: 'neutral'\n" + ] + } + ], + "source": [ + "for idx, (text, label_idx) in enumerate(zip(sample_texts, sample_labels)):\n", + " # Encode input\n", + " encoded = tokenizer(text, return_tensors='pt', truncation=True, padding=True, max_length=128)\n", + " input_ids = encoded['input_ids'].cpu().long()\n", + " attention_mask = encoded['attention_mask'].cpu().long()\n", + "\n", + " # Get embeddings (input for IG)\n", + " inputs_embeds = model.get_input_embeddings()(input_ids)\n", + "\n", + " # Compute attributions\n", + " attributions, delta = ig.attribute(\n", + " inputs=inputs_embeds,\n", + " additional_forward_args=attention_mask,\n", + " target=label_idx,\n", + " return_convergence_delta=True\n", + " )\n", + "\n", + " # Convert input_ids to tokens\n", + " tokens = tokenizer.convert_ids_to_tokens(input_ids.squeeze())\n", + "\n", + " # Mean across embedding dimension\n", + " attributions_sum = attributions.sum(dim=-1).squeeze(0)\n", + " attributions_sum = attributions_sum / torch.norm(attributions_sum)\n", + "\n", + " # Map label index to name\n", + " label_name = label_encoder.inverse_transform([label_idx])[0]\n", + "\n", + " # Plot\n", + " plot_heatmap(tokens, attributions_sum, label_name, idx)\n", + " plot_bar(tokens, attributions_sum, label_name, idx)\n", + "\n", + " print(f\"Saved interpretation plots for sample {idx+1}: '{label_name}'\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "e6a06489-1aba-4f4a-a212-7ee5db8bf22c", + "metadata": {}, + "source": [ + "### Interpretation of Integrated Gradients Attributions" + ] + }, + { + "cell_type": "markdown", + "id": "340fb1bb-8882-4c60-aa4c-4ca4af85fc2f", + "metadata": {}, + "source": [ + "The Integrated Gradients plots (both heatmaps and bar charts) reveal how individual tokens contribute to the model’s classification of each emotion label. Below is an analysis of all five random samples:\n", + "\n", + "Sample 1 – Disapproval\n", + "The tokens **##thic** and **##al** (from the word “unethical”) show the highest positive attribution toward the “disapproval” label. This indicates the model effectively identifies morally charged terms as central to the emotion. Tokens like “corporations” and “universally” also contribute, though to a lesser degree, suggesting the model picks up on critique of systemic entities.\n", + "\n", + "Sample 2 – Neutral\n", + "Interestingly, the word “luckily” has a strong negative attribution, meaning it detracted from a more emotional label being assigned. Despite its connotation, the surrounding context likely flattened its impact. This reflects how nuanced sentiment-neutral language can shift overall classification.\n", + "\n", + "Sample 3 – Neutral\n", + "The word “do” and the **[SEP]** token dominate the attribution, implying that minimal semantic content influenced the prediction. The model appears to correctly assign a neutral label to short, affirmative phrases like “yes definitely do,” focusing more on structure than emotional tone.\n", + "\n", + "Sample 4 – Sadness\n", + "As expected, the word “sad” has the highest attribution, confirming the model’s reliance on explicit emotional language. Subword tokens like **##des** and “**##t**” also contribute meaningfully, which shows the tokenizer’s granularity doesn’t hinder attribution clarity. This attribution pattern demonstrates successful emotion recognition when keywords are present.\n", + "\n", + "Sample 5 – Neutral\n", + "This example includes emotionally suggestive words like “dying” and “dad,” but the overall context lacks sufficient emotional framing. The model attributes some weight to punctuation and the **[SEP]** token, suggesting it interprets the message as incomplete or vague—supporting a neutral label.\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/04_model_comparison.ipynb b/notebooks/04_model_comparison.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..952481a97d8981528c1a43ff891307faae7f47ec --- /dev/null +++ b/notebooks/04_model_comparison.ipynb @@ -0,0 +1,290 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "4032a920-2db8-4977-8b4f-a5a771dd022f", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import os\n", + "\n", + "project_root = os.path.abspath(os.path.join(os.getcwd(), \"..\"))\n", + "sys.path.append(project_root)\n", + "\n", + "from transformers import pipeline\n", + "from src.model_hartmann import load_model as load_hartmann_model, load_tokenizer as load_hartmann_tokenizer\n", + "from src.model_custom import load_model as load_custom_model, load_tokenizer as load_custom_tokenizer" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "525cf57e-4ec3-40fd-aca2-0e9700a73298", + "metadata": {}, + "outputs": [], + "source": [ + "hartmann_model = load_hartmann_model()\n", + "hartmann_tokenizer = load_hartmann_tokenizer()\n", + "\n", + "custom_model = load_custom_model()\n", + "custom_tokenizer = load_custom_tokenizer()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "04f9415c-3d4f-4ac0-8f51-74ec4bd64293", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Hardware accelerator e.g. GPU is available in the environment, but no `device` argument is passed to the `Pipeline` object. Model will be on CPU.\n", + "/opt/anaconda3/lib/python3.12/site-packages/transformers/pipelines/text_classification.py:104: UserWarning: `return_all_scores` is now deprecated, if want a similar functionality use `top_k=None` instead of `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.\n", + " warnings.warn(\n", + "Hardware accelerator e.g. GPU is available in the environment, but no `device` argument is passed to the `Pipeline` object. Model will be on CPU.\n" + ] + } + ], + "source": [ + "# Create pipelines for easy predictions\n", + "hartmann_pipeline = pipeline(\"text-classification\", model=hartmann_model, tokenizer=hartmann_tokenizer, return_all_scores=True)\n", + "custom_pipeline = pipeline(\"text-classification\", model=custom_model, tokenizer=custom_tokenizer, return_all_scores=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3fcdf650-3abc-42a6-b1fd-0129e49d1e68", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "========= Sentence 1 ==========\n", + "Text: I love spending time with my family.\n", + "\n", + "--- Hartmann Model Top 3 Predictions ---\n", + "joy: 0.9883\n", + "sadness: 0.0067\n", + "disgust: 0.0013\n", + "\n", + "--- Pretrained Model Top 3 Predictions ---\n", + "love: 0.9536\n", + "joy: 0.0134\n", + "admiration: 0.0108\n", + "\n", + "\n", + "========= Sentence 2 ==========\n", + "Text: This is the worst day of my life.\n", + "\n", + "--- Hartmann Model Top 3 Predictions ---\n", + "disgust: 0.9805\n", + "anger: 0.0086\n", + "sadness: 0.0055\n", + "\n", + "--- Pretrained Model Top 3 Predictions ---\n", + "anger: 0.3353\n", + "surprise: 0.2010\n", + "disgust: 0.1235\n", + "\n", + "\n", + "========= Sentence 3 ==========\n", + "Text: I'm feeling very nervous about the exam.\n", + "\n", + "--- Hartmann Model Top 3 Predictions ---\n", + "fear: 0.9947\n", + "sadness: 0.0013\n", + "joy: 0.0011\n", + "\n", + "--- Pretrained Model Top 3 Predictions ---\n", + "nervousness: 0.6201\n", + "fear: 0.0828\n", + "embarrassment: 0.0393\n", + "\n", + "\n", + "========= Sentence 4 ==========\n", + "Text: What a beautiful sunset!\n", + "\n", + "--- Hartmann Model Top 3 Predictions ---\n", + "joy: 0.8377\n", + "surprise: 0.1189\n", + "neutral: 0.0221\n", + "\n", + "--- Pretrained Model Top 3 Predictions ---\n", + "admiration: 0.8548\n", + "excitement: 0.0729\n", + "joy: 0.0351\n", + "\n", + "\n", + "========= Sentence 5 ==========\n", + "Text: I feel so disappointed and frustrated with the situation.\n", + "\n", + "--- Hartmann Model Top 3 Predictions ---\n", + "sadness: 0.9310\n", + "anger: 0.0381\n", + "disgust: 0.0158\n", + "\n", + "--- Pretrained Model Top 3 Predictions ---\n", + "disappointment: 0.5645\n", + "annoyance: 0.1864\n", + "anger: 0.0736\n", + "\n", + "\n", + "========= Sentence 6 ==========\n", + "Text: I'm not sure how to feel about this.\n", + "\n", + "--- Hartmann Model Top 3 Predictions ---\n", + "neutral: 0.5698\n", + "disgust: 0.2213\n", + "sadness: 0.0720\n", + "\n", + "--- Pretrained Model Top 3 Predictions ---\n", + "confusion: 0.9011\n", + "optimism: 0.0230\n", + "disapproval: 0.0223\n", + "\n", + "\n", + "========= Sentence 7 ==========\n", + "Text: That was hilarious, I can't stop laughing!\n", + "\n", + "--- Hartmann Model Top 3 Predictions ---\n", + "joy: 0.9336\n", + "surprise: 0.0306\n", + "neutral: 0.0178\n", + "\n", + "--- Pretrained Model Top 3 Predictions ---\n", + "amusement: 0.9551\n", + "joy: 0.0286\n", + "optimism: 0.0032\n", + "\n", + "\n", + "========= Sentence 8 ==========\n", + "Text: I feel completely empty and lost.\n", + "\n", + "--- Hartmann Model Top 3 Predictions ---\n", + "sadness: 0.9808\n", + "neutral: 0.0086\n", + "disgust: 0.0051\n", + "\n", + "--- Pretrained Model Top 3 Predictions ---\n", + "surprise: 0.8055\n", + "disappointment: 0.1067\n", + "optimism: 0.0222\n", + "\n", + "\n", + "========= Sentence 9 ==========\n", + "Text: Your help means a lot to me, thank you!\n", + "\n", + "--- Hartmann Model Top 3 Predictions ---\n", + "joy: 0.9760\n", + "neutral: 0.0104\n", + "surprise: 0.0057\n", + "\n", + "--- Pretrained Model Top 3 Predictions ---\n", + "gratitude: 0.9890\n", + "caring: 0.0014\n", + "sadness: 0.0009\n", + "\n", + "\n", + "========= Sentence 10 ==========\n", + "Text: I'm so angry I could scream.\n", + "\n", + "--- Hartmann Model Top 3 Predictions ---\n", + "anger: 0.9785\n", + "fear: 0.0084\n", + "neutral: 0.0047\n", + "\n", + "--- Pretrained Model Top 3 Predictions ---\n", + "anger: 0.9155\n", + "annoyance: 0.0223\n", + "optimism: 0.0082\n", + "\n", + "\n" + ] + } + ], + "source": [ + "from tabulate import tabulate\n", + "\n", + "goemotions_labels = [\n", + " \"admiration\", \"amusement\", \"anger\", \"annoyance\", \"approval\", \"caring\", \"confusion\", \"curiosity\",\n", + " \"desire\", \"disappointment\", \"disapproval\", \"disgust\", \"embarrassment\", \"excitement\", \"fear\",\n", + " \"gratitude\", \"grief\", \"joy\", \"love\", \"nervousness\", \"optimism\", \"pride\", \"realization\", \"relief\",\n", + " \"remorse\", \"sadness\", \"surprise\", \"neutral\"\n", + "]\n", + "\n", + "\n", + "# Your 10 test sentences\n", + "sentences = [\n", + " \"I love spending time with my family.\",\n", + " \"This is the worst day of my life.\",\n", + " \"I'm feeling very nervous about the exam.\",\n", + " \"What a beautiful sunset!\",\n", + " \"I feel so disappointed and frustrated with the situation.\",\n", + " \"I'm not sure how to feel about this.\",\n", + " \"That was hilarious, I can't stop laughing!\",\n", + " \"I feel completely empty and lost.\",\n", + " \"Your help means a lot to me, thank you!\",\n", + " \"I'm so angry I could scream.\"\n", + "]\n", + "\n", + "# Loop over sentences and collect results\n", + "for i, sentence in enumerate(sentences):\n", + " print(f\"========= Sentence {i+1} ==========\")\n", + " print(f\"Text: {sentence}\\n\")\n", + "\n", + " # Get predictions\n", + " hartmann_results = hartmann_pipeline(sentence, return_all_scores=True)\n", + " custom_results = custom_pipeline(sentence, return_all_scores=True)\n", + "\n", + " # Unwrap the list to get the actual results\n", + " hartmann_results = hartmann_results[0]\n", + " custom_results = custom_results[0]\n", + "\n", + " # Sort and get top 3 predictions for each\n", + " hartmann_top3 = sorted(hartmann_results, key=lambda x: x['score'], reverse=True)[:3]\n", + " custom_top3 = sorted(custom_results, key=lambda x: x['score'], reverse=True)[:3]\n", + "\n", + " # Display Hartmann predictions\n", + " print(\"--- Hartmann Model Top 3 Predictions ---\")\n", + " for res in hartmann_top3:\n", + " print(f\"{res['label']}: {res['score']:.4f}\")\n", + "\n", + " # Display Custom Model predictions\n", + " print(\"\\n--- Pretrained Model Top 3 Predictions ---\")\n", + " for res in custom_top3:\n", + " label_idx = int(res['label'].split(\"_\")[-1])\n", + " emotion = goemotions_labels[label_idx]\n", + " print(f\"{emotion}: {res['score']:.4f}\")\n", + "\n", + " print(\"\\n\")\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + 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end of file diff --git a/outputs/metrics/report.json b/outputs/metrics/report.json new file mode 100644 index 0000000000000000000000000000000000000000..7f2e500a33e70bf531b4317ceca9c45da2c89bce --- /dev/null +++ b/outputs/metrics/report.json @@ -0,0 +1,183 @@ +{ + "admiration": { + "precision": 0.6699186991869919, + "recall": 0.7601476014760148, + "f1-score": 0.7121866897147796, + "support": 542.0 + }, + "amusement": { + "precision": 0.727979274611399, + "recall": 0.8515151515151516, + "f1-score": 0.7849162011173184, + "support": 330.0 + }, + "anger": { + "precision": 0.4329501915708812, + "recall": 0.551219512195122, + "f1-score": 0.48497854077253216, + "support": 205.0 + }, + "annoyance": { + "precision": 0.2983425414364641, + "recall": 0.18620689655172415, + "f1-score": 0.22929936305732485, + "support": 290.0 + }, + "approval": { + "precision": 0.38440111420612816, + "recall": 0.368, + "f1-score": 0.3760217983651226, + "support": 375.0 + }, + "caring": { + "precision": 0.375, + 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index 0000000000000000000000000000000000000000..5930b775a77de79a7ba4f4b11d52f45b0db097fe --- /dev/null +++ b/requirements.txt @@ -0,0 +1,9 @@ +numpy +pandas +scikit-learn +transformers +torch +streamlit +captum +Pillow + diff --git a/save_clean_model.py b/save_clean_model.py new file mode 100644 index 0000000000000000000000000000000000000000..59bf52d05a92f56187f866b259f8dbea40d65b5c --- /dev/null +++ b/save_clean_model.py @@ -0,0 +1,12 @@ +from transformers import AutoModelForSequenceClassification, AutoTokenizer + +# Load the current model from the existing folder +model = AutoModelForSequenceClassification.from_pretrained("outputs/model") +tokenizer = AutoTokenizer.from_pretrained("outputs/model") + +# Save to a new folder without any GPU/device info +model.save_pretrained("outputs/model-clean") +tokenizer.save_pretrained("outputs/model-clean") + +print("Model saved to outputs/model-clean") + diff --git a/src/__pycache__/data_loader.cpython-312.pyc b/src/__pycache__/data_loader.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3e809b0bd1a32e7ada0ce4a328f3276de58684e5 Binary files /dev/null and b/src/__pycache__/data_loader.cpython-312.pyc differ diff --git a/src/__pycache__/evaluate.cpython-312.pyc b/src/__pycache__/evaluate.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8c7b35d5e43f3b45a5aaea39b41c099232006a15 Binary files /dev/null and b/src/__pycache__/evaluate.cpython-312.pyc differ diff --git a/src/__pycache__/model.cpython-312.pyc b/src/__pycache__/model.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..611bd91b27aa1528d8531d39af4544721ed5b285 Binary files /dev/null and b/src/__pycache__/model.cpython-312.pyc differ diff --git a/src/__pycache__/model_custom.cpython-312.pyc b/src/__pycache__/model_custom.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..fcefd797c76c238996582643d493530f560a56e5 Binary files /dev/null and b/src/__pycache__/model_custom.cpython-312.pyc differ diff --git a/src/__pycache__/model_hartmann.cpython-312.pyc b/src/__pycache__/model_hartmann.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..17951a6927e673c6dfc220a222932e383bd62a20 Binary files /dev/null and b/src/__pycache__/model_hartmann.cpython-312.pyc differ diff --git a/src/__pycache__/train.cpython-312.pyc b/src/__pycache__/train.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e9aeaefee4837910f3ae1f7c21ed8a32cc89f40a Binary files /dev/null and b/src/__pycache__/train.cpython-312.pyc differ diff --git a/src/data_loader.py b/src/data_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..63555be0e708e97cd3fbd13ce2d8a7ac2871c0c9 --- /dev/null +++ b/src/data_loader.py @@ -0,0 +1,40 @@ +from datasets import load_dataset +from sklearn.preprocessing import LabelEncoder +from sklearn.model_selection import train_test_split +import pandas as pd + +def load_and_prepare_data(): + # Load dataset directly from Hugging Face + raw_dataset = load_dataset("go_emotions") + + # Convert to DataFrame for easy manipulation + df = pd.DataFrame(raw_dataset["train"]) + + # Grab only examples with a single label (simplification) + df = df[df["labels"].apply(lambda x: len(x) == 1)].reset_index(drop=True) + + # Keep just the first label in list + df["label"] = df["labels"].apply(lambda x: x[0]) + + # Clean text if needed + df["clean_text"] = df["text"].str.lower() + + # Map integer label to string label using Hugging Face's label list + label_names = raw_dataset["train"].features["labels"].feature.names + df["emotion"] = df["label"].apply(lambda x: label_names[x]) + + # Encode emotion names for training + label_encoder = LabelEncoder() + df["label_encoded"] = label_encoder.fit_transform(df["emotion"]) + + # Split into train/test + train_texts, test_texts, train_labels, test_labels = train_test_split( + df["clean_text"].tolist(), + df["label_encoded"].tolist(), + test_size=0.2, + stratify=df["label_encoded"], + random_state=42 + ) + + return train_texts, test_texts, train_labels, test_labels, label_encoder + diff --git a/src/evaluate.py b/src/evaluate.py new file mode 100644 index 0000000000000000000000000000000000000000..23de632a2bf70b3591c3f84a03e919e787b95790 --- /dev/null +++ b/src/evaluate.py @@ -0,0 +1,32 @@ +from sklearn.metrics import accuracy_score, f1_score, classification_report, confusion_matrix +import numpy as np +import json +import matplotlib.pyplot as plt +import seaborn as sns + +# Used during training +def compute_metrics(pred): + labels = pred.label_ids + preds = np.argmax(pred.predictions, axis=1) + return { + "accuracy": accuracy_score(labels, preds), + "f1": f1_score(labels, preds, average="weighted") + } + +# Save classification report +def save_metrics(y_true, y_pred, label_names, out_path="outputs/metrics/report.json"): + report = classification_report(y_true, y_pred, target_names=label_names, output_dict=True) + with open(out_path, "w") as f: + json.dump(report, f, indent=4) + +# Save confusion matrix as image +def save_confusion_matrix(y_true, y_pred, label_names, out_path="outputs/metrics/confusion_matrix.png"): + cm = confusion_matrix(y_true, y_pred) + plt.figure(figsize=(10, 8)) + sns.heatmap(cm, annot=True, fmt='d', xticklabels=label_names, yticklabels=label_names) + plt.xlabel("Predicted") + plt.ylabel("Actual") + plt.title("Confusion Matrix") + plt.tight_layout() + plt.savefig(out_path) + diff --git a/src/model.py b/src/model.py new file mode 100644 index 0000000000000000000000000000000000000000..484ea9ce4fd3d6017ad4301d1c9c56cda1907fd0 --- /dev/null +++ b/src/model.py @@ -0,0 +1,10 @@ +from transformers import BertTokenizer, BertForSequenceClassification + +# Load tokenizer +def get_tokenizer(): + return BertTokenizer.from_pretrained("bert-base-uncased") + +# Load pre-trained BERT with classification head +def get_model(num_labels): + return BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=num_labels) + diff --git a/src/model_custom.py b/src/model_custom.py new file mode 100644 index 0000000000000000000000000000000000000000..06ab563188b2c573e2f83c5c3e2ebff60d1c36ee --- /dev/null +++ b/src/model_custom.py @@ -0,0 +1,12 @@ +from transformers import AutoTokenizer, AutoModelForSequenceClassification + +def load_model(): + model_path = "../outputs/model/" + model = AutoModelForSequenceClassification.from_pretrained(model_path) + return model + +def load_tokenizer(): + model_path = "../outputs/model/" + tokenizer = AutoTokenizer.from_pretrained(model_path) + return tokenizer + diff --git a/src/model_hartmann.py b/src/model_hartmann.py new file mode 100644 index 0000000000000000000000000000000000000000..e85d45ce5a761dd24eb1a97f94281ce68245bbf8 --- /dev/null +++ b/src/model_hartmann.py @@ -0,0 +1,11 @@ +from transformers import AutoTokenizer, AutoModelForSequenceClassification + +def load_tokenizer(): + model_name = "j-hartmann/emotion-english-distilroberta-base" + return AutoTokenizer.from_pretrained(model_name) + +def load_model(): + model_name = "j-hartmann/emotion-english-distilroberta-base" + model = AutoModelForSequenceClassification.from_pretrained(model_name) + return model + diff --git a/src/train.py b/src/train.py new file mode 100644 index 0000000000000000000000000000000000000000..781eec64f35ccf1c54b0e0b3f07ec096f6e93460 --- /dev/null +++ b/src/train.py @@ -0,0 +1,29 @@ +from transformers import Trainer, TrainingArguments + +def get_training_args(output_dir="outputs/model"): + return TrainingArguments( + output_dir=output_dir, + evaluation_strategy="epoch", + save_strategy="epoch", + learning_rate=2e-5, + per_device_train_batch_size=16, + per_device_eval_batch_size=16, + num_train_epochs=3, + weight_decay=0.01, + logging_dir="outputs/logs", + logging_steps=10, + load_best_model_at_end=True, + metric_for_best_model="f1" + ) + +def train_model(model, args, train_dataset, val_dataset, compute_metrics): + trainer = Trainer( + model=model, + args=args, + train_dataset=train_dataset, + eval_dataset=val_dataset, + compute_metrics=compute_metrics + ) + trainer.train() + return trainer +