Move project to Hugging Space

README.md

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+# 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.
+

app/app.py

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+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}")
+

notebooks/.ipynb_checkpoints/01_exploration-checkpoint.ipynb

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+{
+ "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": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>text</th>\n",
+       "      <th>labels</th>\n",
+       "      <th>id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>My favourite food is anything I didn't have to...</td>\n",
+       "      <td>[27]</td>\n",
+       "      <td>eebbqej</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Now if he does off himself, everyone will thin...</td>\n",
+       "      <td>[27]</td>\n",
+       "      <td>ed00q6i</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>WHY THE FUCK IS BAYLESS ISOING</td>\n",
+       "      <td>[2]</td>\n",
+       "      <td>eezlygj</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>To make her feel threatened</td>\n",
+       "      <td>[14]</td>\n",
+       "      <td>ed7ypvh</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Dirty Southern Wankers</td>\n",
+       "      <td>[3]</td>\n",
+       "      <td>ed0bdzj</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "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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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot for the number of occurences of each emotion in the training set\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "pd.Series(label_distribution).sort_values(ascending=False).plot(kind='bar')\n",
+    "plt.title(\"GoEmotions Label Distribution\")\n",
+    "plt.ylabel(\"Count\")\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6d28b28c-d87f-41e5-afd2-cc3257dd974b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>text</th>\n",
+       "      <th>labels</th>\n",
+       "      <th>id</th>\n",
+       "      <th>clean_text</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>My favourite food is anything I didn't have to...</td>\n",
+       "      <td>[27]</td>\n",
+       "      <td>eebbqej</td>\n",
+       "      <td>my favourite food is anything i didnt have to ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Now if he does off himself, everyone will thin...</td>\n",
+       "      <td>[27]</td>\n",
+       "      <td>ed00q6i</td>\n",
+       "      <td>now if he does off himself everyone will think...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>WHY THE FUCK IS BAYLESS ISOING</td>\n",
+       "      <td>[2]</td>\n",
+       "      <td>eezlygj</td>\n",
+       "      <td>why the fuck is bayless isoing</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>To make her feel threatened</td>\n",
+       "      <td>[14]</td>\n",
+       "      <td>ed7ypvh</td>\n",
+       "      <td>to make her feel threatened</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Dirty Southern Wankers</td>\n",
+       "      <td>[3]</td>\n",
+       "      <td>ed0bdzj</td>\n",
+       "      <td>dirty southern wankers</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>OmG pEyToN iSn'T gOoD eNoUgH tO hElP uS iN tHe...</td>\n",
+       "      <td>[26]</td>\n",
+       "      <td>edvnz26</td>\n",
+       "      <td>omg peyton isnt good enough to help us in the ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Yes I heard abt the f bombs! That has to be wh...</td>\n",
+       "      <td>[15]</td>\n",
+       "      <td>ee3b6wu</td>\n",
+       "      <td>yes i heard abt the f bombs that has to be why...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>We need more boards and to create a bit more s...</td>\n",
+       "      <td>[8, 20]</td>\n",
+       "      <td>ef4qmod</td>\n",
+       "      <td>we need more boards and to create a bit more s...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Damn youtube and outrage drama is super lucrat...</td>\n",
+       "      <td>[0]</td>\n",
+       "      <td>ed8wbdn</td>\n",
+       "      <td>damn youtube and outrage drama is super lucrat...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>It might be linked to the trust factor of your...</td>\n",
+       "      <td>[27]</td>\n",
+       "      <td>eczgv1o</td>\n",
+       "      <td>it might be linked to the trust factor of your...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "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
+}

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",
+       "    <div>\n",
+       "      \n",
+       "      <progress value='5448' max='5448' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      [5448/5448 1:46:28, Epoch 3/3]\n",
+       "    </div>\n",
+       "    <table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       " <tr style=\"text-align: left;\">\n",
+       "      <th>Epoch</th>\n",
+       "      <th>Training Loss</th>\n",
+       "      <th>Validation Loss</th>\n",
+       "      <th>Accuracy</th>\n",
+       "      <th>F1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>1.358900</td>\n",
+       "      <td>1.335635</td>\n",
+       "      <td>0.613467</td>\n",
+       "      <td>0.579882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>0.947100</td>\n",
+       "      <td>1.284574</td>\n",
+       "      <td>0.615671</td>\n",
+       "      <td>0.601428</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>0.970400</td>\n",
+       "      <td>1.297894</td>\n",
+       "      <td>0.617048</td>\n",
+       "      <td>0.606042</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table><p>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      \n",
+       "      <progress value='5448' max='5448' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      [5448/5448 1:35:20, Epoch 3/3]\n",
+       "    </div>\n",
+       "    <table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       " <tr style=\"text-align: left;\">\n",
+       "      <th>Epoch</th>\n",
+       "      <th>Training Loss</th>\n",
+       "      <th>Validation Loss</th>\n",
+       "      <th>Accuracy</th>\n",
+       "      <th>F1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>0.907200</td>\n",
+       "      <td>1.365916</td>\n",
+       "      <td>0.602313</td>\n",
+       "      <td>0.595804</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>0.549100</td>\n",
+       "      <td>1.488130</td>\n",
+       "      <td>0.595566</td>\n",
+       "      <td>0.591464</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>0.514400</td>\n",
+       "      <td>1.593286</td>\n",
+       "      <td>0.591297</td>\n",
+       "      <td>0.589066</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table><p>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "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
+}

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
+}

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": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>text</th>\n",
+       "      <th>labels</th>\n",
+       "      <th>id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>My favourite food is anything I didn't have to...</td>\n",
+       "      <td>[27]</td>\n",
+       "      <td>eebbqej</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Now if he does off himself, everyone will thin...</td>\n",
+       "      <td>[27]</td>\n",
+       "      <td>ed00q6i</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>WHY THE FUCK IS BAYLESS ISOING</td>\n",
+       "      <td>[2]</td>\n",
+       "      <td>eezlygj</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>To make her feel threatened</td>\n",
+       "      <td>[14]</td>\n",
+       "      <td>ed7ypvh</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Dirty Southern Wankers</td>\n",
+       "      <td>[3]</td>\n",
+       "      <td>ed0bdzj</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "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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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot for the number of occurences of each emotion in the training set\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "pd.Series(label_distribution).sort_values(ascending=False).plot(kind='bar')\n",
+    "plt.title(\"GoEmotions Label Distribution\")\n",
+    "plt.ylabel(\"Count\")\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6d28b28c-d87f-41e5-afd2-cc3257dd974b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>text</th>\n",
+       "      <th>labels</th>\n",
+       "      <th>id</th>\n",
+       "      <th>clean_text</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>My favourite food is anything I didn't have to...</td>\n",
+       "      <td>[27]</td>\n",
+       "      <td>eebbqej</td>\n",
+       "      <td>my favourite food is anything i didnt have to ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Now if he does off himself, everyone will thin...</td>\n",
+       "      <td>[27]</td>\n",
+       "      <td>ed00q6i</td>\n",
+       "      <td>now if he does off himself everyone will think...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>WHY THE FUCK IS BAYLESS ISOING</td>\n",
+       "      <td>[2]</td>\n",
+       "      <td>eezlygj</td>\n",
+       "      <td>why the fuck is bayless isoing</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>To make her feel threatened</td>\n",
+       "      <td>[14]</td>\n",
+       "      <td>ed7ypvh</td>\n",
+       "      <td>to make her feel threatened</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Dirty Southern Wankers</td>\n",
+       "      <td>[3]</td>\n",
+       "      <td>ed0bdzj</td>\n",
+       "      <td>dirty southern wankers</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>OmG pEyToN iSn'T gOoD eNoUgH tO hElP uS iN tHe...</td>\n",
+       "      <td>[26]</td>\n",
+       "      <td>edvnz26</td>\n",
+       "      <td>omg peyton isnt good enough to help us in the ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Yes I heard abt the f bombs! That has to be wh...</td>\n",
+       "      <td>[15]</td>\n",
+       "      <td>ee3b6wu</td>\n",
+       "      <td>yes i heard abt the f bombs that has to be why...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>We need more boards and to create a bit more s...</td>\n",
+       "      <td>[8, 20]</td>\n",
+       "      <td>ef4qmod</td>\n",
+       "      <td>we need more boards and to create a bit more s...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Damn youtube and outrage drama is super lucrat...</td>\n",
+       "      <td>[0]</td>\n",
+       "      <td>ed8wbdn</td>\n",
+       "      <td>damn youtube and outrage drama is super lucrat...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>It might be linked to the trust factor of your...</td>\n",
+       "      <td>[27]</td>\n",
+       "      <td>eczgv1o</td>\n",
+       "      <td>it might be linked to the trust factor of your...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "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
+}

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",
+       "    <div>\n",
+       "      \n",
+       "      <progress value='5448' max='5448' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      [5448/5448 1:46:28, Epoch 3/3]\n",
+       "    </div>\n",
+       "    <table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       " <tr style=\"text-align: left;\">\n",
+       "      <th>Epoch</th>\n",
+       "      <th>Training Loss</th>\n",
+       "      <th>Validation Loss</th>\n",
+       "      <th>Accuracy</th>\n",
+       "      <th>F1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>1.358900</td>\n",
+       "      <td>1.335635</td>\n",
+       "      <td>0.613467</td>\n",
+       "      <td>0.579882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>0.947100</td>\n",
+       "      <td>1.284574</td>\n",
+       "      <td>0.615671</td>\n",
+       "      <td>0.601428</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>0.970400</td>\n",
+       "      <td>1.297894</td>\n",
+       "      <td>0.617048</td>\n",
+       "      <td>0.606042</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table><p>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      \n",
+       "      <progress value='5448' max='5448' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      [5448/5448 1:35:20, Epoch 3/3]\n",
+       "    </div>\n",
+       "    <table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       " <tr style=\"text-align: left;\">\n",
+       "      <th>Epoch</th>\n",
+       "      <th>Training Loss</th>\n",
+       "      <th>Validation Loss</th>\n",
+       "      <th>Accuracy</th>\n",
+       "      <th>F1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>0.907200</td>\n",
+       "      <td>1.365916</td>\n",
+       "      <td>0.602313</td>\n",
+       "      <td>0.595804</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>0.549100</td>\n",
+       "      <td>1.488130</td>\n",
+       "      <td>0.595566</td>\n",
+       "      <td>0.591464</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>0.514400</td>\n",
+       "      <td>1.593286</td>\n",
+       "      <td>0.591297</td>\n",
+       "      <td>0.589066</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table><p>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "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
+}

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"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

outputs/metrics/.ipynb_checkpoints/report-checkpoint.json

@@ -0,0 +1,183 @@
+{
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+        "recall": 0.7601476014760148,
+        "f1-score": 0.7121866897147796,
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+    },
+    "amusement": {
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+    },
+    "annoyance": {
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+        "recall": 0.18620689655172415,
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+    },
+    "approval": {
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+        "recall": 0.368,
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+    "curiosity": {
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+        "recall": 0.6623781676413255,
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+    "accuracy": 0.6023134122831176,
+    "macro avg": {
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+        "f1-score": 0.48261389641901975,
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+    "weighted avg": {
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+        "recall": 0.6023134122831176,
+        "f1-score": 0.595804330387673,
+        "support": 7262.0
+    }
+}

outputs/metrics/report.json

@@ -0,0 +1,183 @@
+{
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+        "recall": 0.7601476014760148,
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+        "recall": 0.60431654676259,
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+    "nervousness": {
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+        "recall": 0.6623781676413255,
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+    "pride": {
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+        "recall": 0.1,
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+    "realization": {
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+        "recall": 0.15384615384615385,
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+    "relief": {
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+        "f1-score": 0.0,
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+    "remorse": {
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+    },
+    "sadness": {
+        "precision": 0.5540540540540541,
+        "recall": 0.5030674846625767,
+        "f1-score": 0.5273311897106109,
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+    },
+    "surprise": {
+        "precision": 0.5688622754491018,
+        "recall": 0.6597222222222222,
+        "f1-score": 0.6109324758842444,
+        "support": 144.0
+    },
+    "accuracy": 0.6023134122831176,
+    "macro avg": {
+        "precision": 0.5125054563961818,
+        "recall": 0.47883579303055207,
+        "f1-score": 0.48261389641901975,
+        "support": 7262.0
+    },
+    "weighted avg": {
+        "precision": 0.6008835076974202,
+        "recall": 0.6023134122831176,
+        "f1-score": 0.595804330387673,
+        "support": 7262.0
+    }
+}

outputs/model-old/.ipynb_checkpoints/special_tokens_map-checkpoint.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

outputs/model-old/config.json

@@ -0,0 +1,87 @@
+{
+  "_name_or_path": "bert-base-uncased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
+    "5": "LABEL_5",
+    "6": "LABEL_6",
+    "7": "LABEL_7",
+    "8": "LABEL_8",
+    "9": "LABEL_9",
+    "10": "LABEL_10",
+    "11": "LABEL_11",
+    "12": "LABEL_12",
+    "13": "LABEL_13",
+    "14": "LABEL_14",
+    "15": "LABEL_15",
+    "16": "LABEL_16",
+    "17": "LABEL_17",
+    "18": "LABEL_18",
+    "19": "LABEL_19",
+    "20": "LABEL_20",
+    "21": "LABEL_21",
+    "22": "LABEL_22",
+    "23": "LABEL_23",
+    "24": "LABEL_24",
+    "25": "LABEL_25",
+    "26": "LABEL_26",
+    "27": "LABEL_27"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_10": 10,
+    "LABEL_11": 11,
+    "LABEL_12": 12,
+    "LABEL_13": 13,
+    "LABEL_14": 14,
+    "LABEL_15": 15,
+    "LABEL_16": 16,
+    "LABEL_17": 17,
+    "LABEL_18": 18,
+    "LABEL_19": 19,
+    "LABEL_2": 2,
+    "LABEL_20": 20,
+    "LABEL_21": 21,
+    "LABEL_22": 22,
+    "LABEL_23": 23,
+    "LABEL_24": 24,
+    "LABEL_25": 25,
+    "LABEL_26": 26,
+    "LABEL_27": 27,
+    "LABEL_3": 3,
+    "LABEL_4": 4,
+    "LABEL_5": 5,
+    "LABEL_6": 6,
+    "LABEL_7": 7,
+    "LABEL_8": 8,
+    "LABEL_9": 9
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.45.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

outputs/model-old/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a22a9c126ae06332bdd125ee414560a9539321c42dcfd4882c50f56b7d5a8c35
+size 438038624

outputs/model-old/special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

outputs/model-old/tokenizer_config.json

@@ -0,0 +1,57 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

outputs/model/config.json

@@ -0,0 +1,87 @@
+{
+  "_name_or_path": "outputs/model",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
+    "5": "LABEL_5",
+    "6": "LABEL_6",
+    "7": "LABEL_7",
+    "8": "LABEL_8",
+    "9": "LABEL_9",
+    "10": "LABEL_10",
+    "11": "LABEL_11",
+    "12": "LABEL_12",
+    "13": "LABEL_13",
+    "14": "LABEL_14",
+    "15": "LABEL_15",
+    "16": "LABEL_16",
+    "17": "LABEL_17",
+    "18": "LABEL_18",
+    "19": "LABEL_19",
+    "20": "LABEL_20",
+    "21": "LABEL_21",
+    "22": "LABEL_22",
+    "23": "LABEL_23",
+    "24": "LABEL_24",
+    "25": "LABEL_25",
+    "26": "LABEL_26",
+    "27": "LABEL_27"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_10": 10,
+    "LABEL_11": 11,
+    "LABEL_12": 12,
+    "LABEL_13": 13,
+    "LABEL_14": 14,
+    "LABEL_15": 15,
+    "LABEL_16": 16,
+    "LABEL_17": 17,
+    "LABEL_18": 18,
+    "LABEL_19": 19,
+    "LABEL_2": 2,
+    "LABEL_20": 20,
+    "LABEL_21": 21,
+    "LABEL_22": 22,
+    "LABEL_23": 23,
+    "LABEL_24": 24,
+    "LABEL_25": 25,
+    "LABEL_26": 26,
+    "LABEL_27": 27,
+    "LABEL_3": 3,
+    "LABEL_4": 4,
+    "LABEL_5": 5,
+    "LABEL_6": 6,
+    "LABEL_7": 7,
+    "LABEL_8": 8,
+    "LABEL_9": 9
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.45.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

outputs/model/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a22a9c126ae06332bdd125ee414560a9539321c42dcfd4882c50f56b7d5a8c35
+size 438038624

outputs/model/special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

outputs/model/tokenizer_config.json

@@ -0,0 +1,57 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

requirements.txt

@@ -0,0 +1,9 @@
+numpy
+pandas
+scikit-learn
+transformers
+torch
+streamlit
+captum
+Pillow
+

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")
+