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.streamlit/config.toml

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+[theme]
+base="light"

LICENSE

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README.md

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+---
+title: Huggingface Multi Inference Rank Eval
+emoji: 🤔
+colorFrom: yellow
+colorTo: purple
+sdk: streamlit
+sdk_version: 1.10.0
+app_file: ./app/main_page.py
+pinned: false
+license: cc
+---
+
+# huggingface_multi_inference_rank_eval
+**This app lets the users play around with Huggingface models on a prommpted multiple choice QA inference.**
+
+Recenet researches like [GPT-3](https://arxiv.org/abs/2005.14165), [FLAN](https://arxiv.org/abs/2109.01652), and [T0](https://arxiv.org/abs/2110.08207) showed a promising direction in zero-shot generalization via prompting. Rathern than pretraining on a huge dataset and finetuning a model on downstream task, we can directly ask a question to a model by prompting a model on a task!
+
+
+<p align="center">
+  <img src="assets/demo.png" width="800"/>
+</p>
+
+This app lets users interact with various models hosted on [Huggingface models](https://huggingface.co/models) and ask a multiple choice question. Models rank the choices with their log probabilities and pick the choice with highest log probability. We currently supoort [CausalLM](https://huggingface.co/docs/transformers/model_doc/auto#transformers.AutoModelForCausalLM) and [Seq2SeqLM](https://huggingface.co/docs/transformers/model_doc/auto#transformers.AutoModelForSeq2SeqLM).
+
+e.g.
+```
+Question: Huggingface is awesome. True or False?
+Answer choices: True, False
+Prediction: True
+```
+
+**You can access the [hosted version](https://huggingface.co/spaces/kkawamu1/huggingface_multi_inference_rank_eval).**
+
+## Setup
+Clone the repository, install the required packages and run:
+```bash
+streamlit run ./app/main_page.py
+```
+
+
+## Reference
+A chunk of codes used for this projects is taken and insipred from the following works and their related repository:
+```bibtex
+@inproceedings{sanh2022multitask,
+    title={Multitask Prompted Training Enables Zero-Shot Task Generalization},
+    author={Victor Sanh and Albert Webson and Colin Raffel and Stephen Bach and Lintang Sutawika and Zaid Alyafeai and Antoine Chaffin and Arnaud Stiegler and Arun Raja and Manan Dey and M Saiful Bari and Canwen Xu and Urmish Thakker and Shanya Sharma Sharma and Eliza Szczechla and Taewoon Kim and Gunjan Chhablani and Nihal Nayak and Debajyoti Datta and Jonathan Chang and Mike Tian-Jian Jiang and Han Wang and Matteo Manica and Sheng Shen and Zheng Xin Yong and Harshit Pandey and Rachel Bawden and Thomas Wang and Trishala Neeraj and Jos Rozen and Abheesht Sharma and Andrea Santilli and Thibault Fevry and Jason Alan Fries and Ryan Teehan and Teven Le Scao and Stella Biderman and Leo Gao and Thomas Wolf and Alexander M Rush},
+    booktitle={International Conference on Learning Representations},
+    year={2022}
+    url={https://openreview.net/forum?id=9Vrb9D0WI4}
+```
+```bibtex
+@software{eval-harness,
+  author       = {Gao, Leo and
+                  Tow, Jonathan and
+                  Biderman, Stella and
+                  Black, Sid and
+                  DiPofi, Anthony and
+                  Foster, Charles and
+                  Golding, Laurence and
+                  Hsu, Jeffrey and
+                  McDonell, Kyle and
+                  Muennighoff, Niklas and
+                  Phang, Jason and
+                  Reynolds, Laria and
+                  Tang, Eric and
+                  Thite, Anish and
+                  Wang, Ben and
+                  Wang, Kevin and
+                  Zou, Andy},
+  title        = {A framework for few-shot language model evaluation},
+  month        = sep,
+  year         = 2021,
+  publisher    = {Zenodo},
+  version      = {v0.0.1},
+  doi          = {10.5281/zenodo.5371628},
+  url          = {https://doi.org/10.5281/zenodo.5371628}
+}
+```
+For style, 
+```
+https://fossheim.io/writing/posts/css-text-gradient/
+https://css-tricks.com/css-hover-effects-background-masks-3d/
+```
+
+

app/evaluation_scripts/run_eval.py

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+import torch
+from accelerate import Accelerator
+from transformers import (AutoModelForCausalLM, AutoModelForSeq2SeqLM,
+                          AutoTokenizer, set_seed)
+
+
+def multi_inference_rank_eval(model_name_or_path, auto_class, ex_answer_choices, context):
+    accelerator = Accelerator()
+    set_seed(42)
+    model_name = model_name_or_path
+    if auto_class == 'Seq2SeqLM':
+
+        # e.g. 'google/t5-small-lm-adapt'
+        model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+        tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
+    else:
+        # e.g. 'gpt2'
+        model = AutoModelForCausalLM.from_pretrained(model_name)
+        tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
+
+    if tokenizer.pad_token is None:
+        for token in [tokenizer.eos_token, tokenizer.bos_token, tokenizer.sep_token]:
+            if token is not None:
+                tokenizer.pad_token = token
+        if tokenizer.pad_token is None:
+            raise ValueError("Please define a pad token id.")
+
+    padding = False
+
+    if auto_class == 'Seq2SeqLM':
+        def preprocess_function(context, ex_answer_choices):
+            input_texts = []
+            answer_choices_texts = []
+            input_texts.append(context)
+            answer_choices_texts.append(
+                [' ' + ans for ans in ex_answer_choices])
+
+            tokenized_inputs = tokenizer(
+                input_texts,
+                padding=padding,
+                max_length=1024,
+                truncation=True,
+                add_special_tokens=False,
+            )
+
+            tokenized_targets = [
+                tokenizer(
+                    ans_choi,
+                    padding=True,
+                    max_length=256,
+                    truncation=True,
+                )
+                for ans_choi in answer_choices_texts
+            ]
+
+            features = {
+                k: [
+                    [elem for _ in range(
+                        len(tokenized_targets[idx]["input_ids"]))]
+                    for idx, elem in enumerate(v)
+                ]
+                for k, v in tokenized_inputs.items()
+            }
+
+            features["labels"] = [
+                tokenized_targets[0]["input_ids"]
+            ]
+
+            features["labels_attention_mask"] = [
+                tokenized_targets[0]["attention_mask"]
+            ]
+            return features
+    else:
+        def preprocess_function(context, ex_answer_choices):
+            input_texts = []
+            answer_choices_texts = []
+            input_texts.append(context)
+            answer_choices_texts.append(
+                [' ' + ans for ans in ex_answer_choices])
+
+            tokenized_inputs = tokenizer(
+                input_texts,
+                padding=padding,
+                max_length=1024,
+                truncation=True,
+                add_special_tokens=False,
+            )
+
+            tokenized_targets = [
+                tokenizer(
+                    ans_choi,
+                    padding=True,
+                    max_length=256,
+                    truncation=True,
+                )
+                for ans_choi in answer_choices_texts
+            ]
+
+            features = {
+                k: [
+                    [elem for _ in range(
+                        len(tokenized_targets[idx]["input_ids"]))]
+                    for idx, elem in enumerate(v)
+                ]
+                for k, v in tokenized_inputs.items()
+            }
+
+            features["labels"] = [
+                tokenized_targets[0]["input_ids"]
+            ]
+
+            features["labels_attention_mask"] = [
+                tokenized_targets[0]["attention_mask"]
+            ]
+
+            features["labels"] = [
+                [features["input_ids"][0][i][1:] + tokenized_targets[0]["input_ids"][i]
+                    for i in range(len(tokenized_targets[0]["input_ids"]))]
+            ]
+            features["input_ids"] = [
+                [features["input_ids"][0][i] + tokenized_targets[0]["input_ids"][i][:-1]
+                    for i in range(len(tokenized_targets[0]["input_ids"]))]
+            ]
+
+            features["labels_attention_mask"] = [
+                [[0] * (len(features["attention_mask"][0][i])-1) + tokenized_targets[0]
+                 ["attention_mask"][i] for i in range(len(tokenized_targets[0]["input_ids"]))]
+            ]
+
+            features["attention_mask"] = [
+                [features["attention_mask"][0][i] + tokenized_targets[0]["attention_mask"][i][:-1]
+                    for i in range(len(tokenized_targets[0]["input_ids"]))]
+            ]
+
+            return features
+
+    device = accelerator.device
+    model.to(device)
+    batch = preprocess_function(context, ex_answer_choices)
+    batch = {
+        k: torch.tensor(batch[k][0]).to(device)
+        for k in batch.keys()
+    }
+
+    model.eval()
+    with torch.no_grad():
+        model_inputs = {
+            k: batch[k]
+            for k in (["input_ids", "attention_mask", "labels"] if auto_class == 'Seq2SeqLM' else ["input_ids", "attention_mask"])
+        }
+
+        logits = model(**model_inputs).logits
+        masked_log_probs = batch["labels_attention_mask"].unsqueeze(
+            -1) * torch.log_softmax(logits, dim=-1)
+        seq_token_log_probs = torch.gather(
+            masked_log_probs, -1, batch["labels"].unsqueeze(-1))
+        seq_log_prob = seq_token_log_probs.squeeze(dim=-1).sum(dim=-1)
+        seq_log_prob = seq_log_prob.view(1, -1)
+        predictions = seq_log_prob.argmax(dim=-1)
+
+    predictions = accelerator.gather(predictions)
+    return predictions.item()
+
+
+if __name__ == "__main__":
+    multi_inference_rank_eval('google/t5-small-lm-adapt', 'Seq2SeqLM',
+                              ['True', 'False', 'True', 'Ken'], 'I am Ken. True or False')
+    # multi_inference_rank_eval('gpt2', 'CausalLM', ['True', 'False', 'True', 'Ken'], 'I am Ken. True or False')

app/main_page.py

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+import streamlit as st
+import streamlit.components.v1 as stc
+
+st.set_page_config(
+    page_title="Example",  layout="wide"
+)
+
+#style taken from https://fossheim.io/writing/posts/css-text-gradient/
+stc.html("""
+        <meta name="viewport" content="width=device-width, initial-scale=1">
+        <style>
+        
+        body {
+        margin: 0;
+        font-family: Arial;
+        }
+        .gradient-text {
+        /* Fallback: Set a background color. */
+        background-color: red;
+        
+        /* Create the gradient. */
+        background-image: linear-gradient(45deg, #f3ec78, #af4261);
+        
+        /* Set the background size and repeat properties. */
+        background-size: 100%;
+        background-repeat: repeat;
+
+        /* Use the text as a mask for the background. */
+        /* This will show the gradient as a text color rather than element bg. */
+        -webkit-background-clip: text;
+        -webkit-text-fill-color: transparent; 
+        -moz-background-clip: text;
+        -moz-text-fill-color: transparent;
+        }
+
+
+
+        h1 {
+        font-family: "Archivo Black", sans-serif;
+        font-weight: normal;
+        font-size: 8em;
+        text-align: center;
+        margin-bottom: 0;
+        margin-bottom: -0.25em;
+        }
+        </style>
+
+        </head>
+        <body>
+        
+        <h1 class="gradient-text">Multiple Choice<br>QA<br></h1>
+
+
+
+        </body>
+        </html>
+
+        """, height=1000)
+
+st.sidebar.markdown("# Home Page 🤗")

app/pages/02_evaluation.py

@@ -0,0 +1,105 @@
+
+import streamlit as st
+import requests
+from typing import Dict
+import os
+import sys
+
+sys.path.append(os.path.join(os.path.dirname(__file__), "..", ".."))
+from app.evaluation_scripts.run_eval import multi_inference_rank_eval
+
+
+st.set_page_config(layout="wide")
+st.markdown(f'<p style="text-align:center;background-image: linear-gradient(to right,#1aa3ff, #00ff00);color:#ffffff;font-size: 4em;;border-radius:2%;-webkit-background-clip: text;-webkit-text-fill-color: transparent;">Submit your question here</p>', unsafe_allow_html=True)
+
+
+st.sidebar.markdown("# Evaluation 🤔")
+st.markdown(
+    '<style>div[data-testid="stHorizontalBlock"] > div:first-of-type {border:10px;padding:30px;border-radius: 10px;;background-image: linear-gradient(45deg, #1aa3ff, #234599);}</style>', unsafe_allow_html=True)
+st.markdown(
+    '<style>div[data-testid="stHorizontalBlock"] > div:nth-of-type(2)  {border:10px;padding:30px;border-radius: 10px;background-image: linear-gradient(45deg, #f3ec78, #023611);}</style>', unsafe_allow_html=True)
+
+
+
+INCLUDED_USERS = ['google', 'EleutherAI',
+                  "bigscience", "facebook", "openai", "microsoft"]
+
+PIPELINE_TAG_TO_TASKS = {
+    'text-generation': "CausalLM", 'text2text-generation': "Seq2SeqLM"}
+
+
+@st.cache
+def fetch_model_info_from_huggingface_api() -> Dict[str, Dict[str, str]]:
+    requests.get("https://huggingface.co")
+    response = requests.get("https://huggingface.co/api/models")
+    tags = response.json()
+    model_to_model_id = {}
+    model_to_pipeline_tag = {}
+
+    for model in tags:
+        model_name = model['modelId']
+        is_community_model = "/" in model_name
+        if is_community_model:
+            user = model_name.split("/")[0]
+            if user not in INCLUDED_USERS:
+                continue
+        if "pipeline_tag" in model and model["pipeline_tag"] in list(PIPELINE_TAG_TO_TASKS.keys()):
+            model_to_model_id[model['id']] = model['modelId']
+            model_to_pipeline_tag[model['id']
+                                  ] = PIPELINE_TAG_TO_TASKS[model["pipeline_tag"]]
+    return model_to_pipeline_tag
+
+
+model_to_auto_class = fetch_model_info_from_huggingface_api()
+
+col1, col2 = st.columns([3, 2])
+user_input = {}
+with col1:
+    st.header("Question")
+    user_input['context'] = st.text_input(
+        label='Write your question. You may explicity mention the answer choices in the prompt.', value='Huggingface is awesome. True or False?')
+    user_input['answer_choices_texts'] = st.text_input(
+        label='Add answer choices in text spearated by a comma and a space.', value='True, False')
+    user_input['answer_choices_texts'] = user_input['answer_choices_texts'].split(
+        ', ')
+
+
+with col2:
+    st.header("Model Config")
+    user_input['model'] = st.selectbox(
+        "Which model?", list(model_to_auto_class.keys()))
+    user_input['auto_class'] = model_to_auto_class[user_input['model']]
+col4, col5 = st.columns(2)
+with col5:
+    #style taken from https://css-tricks.com/css-hover-effects-background-masks-3d/
+    st.markdown("""<style> 
+    div.stButton > button:first-child  {
+        width: 100%;
+        border: 4px solid;
+        border-image: repeating-linear-gradient(135deg,#F8CA00 0 10px,#E97F02 0 20px,#BD1550 0 30px) 8;
+        -webkit-mask: 
+            conic-gradient(from 180deg at top 8px right 8px, #0000 90deg,#000 0)
+            var(--_i,200%) 0  /200% var(--_i,8px) border-box no-repeat,
+            conic-gradient(at bottom 8px left  8px,  #0000 90deg,#000 0)
+            0   var(--_i,200%)/var(--_i,8px) 200% border-box no-repeat,
+            linear-gradient(#000 0 0) padding-box no-repeat;
+        transition: .3s, -webkit-mask-position .3s .3s;
+        
+    }
+    button:hover {
+        --_i: 100%;
+        color: #CC333F;
+        transition: .3s, -webkit-mask-size .3s .3s;
+    }
+    </style>""", unsafe_allow_html=True)
+    st.header("Submit task")
+    submit = st.button('Submit')
+
+with col4:
+    st.header("Result")
+    if submit:
+        with st.spinner('Wait for it...'):
+            prediction = multi_inference_rank_eval(
+                user_input['model'], user_input['auto_class'], user_input['answer_choices_texts'], user_input['context'])
+        # print(prediction)
+        st.markdown(f"### {user_input['answer_choices_texts'][prediction]}")

app/pages/03_reference.py

@@ -0,0 +1,43 @@
+import streamlit as st
+
+st.set_page_config(layout="wide")
+st.markdown(f'<p style="text-align:center;background-image: linear-gradient(to right,#d9306e, #412f8f);color:#ffffff;font-size: 4em;;border-radius:2%;-webkit-background-clip: text;-webkit-text-fill-color: transparent;">Reference</p>', unsafe_allow_html=True)
+
+st.markdown('### A chunk of codes used for this projects is taken and insipred from the following works and their related repository:')
+
+st.markdown("""1. @inproceedings{sanh2022multitask,
+    title={Multitask Prompted Training Enables Zero-Shot Task Generalization},
+    author={Victor Sanh and Albert Webson and Colin Raffel and Stephen Bach and Lintang Sutawika and Zaid Alyafeai and Antoine Chaffin and Arnaud Stiegler and Arun Raja and Manan Dey and M Saiful Bari and Canwen Xu and Urmish Thakker and Shanya Sharma Sharma and Eliza Szczechla and Taewoon Kim and Gunjan Chhablani and Nihal Nayak and Debajyoti Datta and Jonathan Chang and Mike Tian-Jian Jiang and Han Wang and Matteo Manica and Sheng Shen and Zheng Xin Yong and Harshit Pandey and Rachel Bawden and Thomas Wang and Trishala Neeraj and Jos Rozen and Abheesht Sharma and Andrea Santilli and Thibault Fevry and Jason Alan Fries and Ryan Teehan and Teven Le Scao and Stella Biderman and Leo Gao and Thomas Wolf and Alexander M Rush},
+    booktitle={International Conference on Learning Representations},
+    year={2022}
+    url={https://openreview.net/forum?id=9Vrb9D0WI4}""")
+    
+st.markdown("""2. @software{eval-harness, author = {Gao, Leo and
+                  Tow, Jonathan and
+                  Biderman, Stella and
+                  Black, Sid and
+                  DiPofi, Anthony and
+                  Foster, Charles and
+                  Golding, Laurence and
+                  Hsu, Jeffrey and
+                  McDonell, Kyle and
+                  Muennighoff, Niklas and
+                  Phang, Jason and
+                  Reynolds, Laria and
+                  Tang, Eric and
+                  Thite, Anish and
+                  Wang, Ben and
+                  Wang, Kevin and
+                  Zou, Andy},
+                  title        = {A framework for few-shot language model evaluation},
+                  month        = sep,
+                  year         = 2021,
+                  publisher    = {Zenodo},
+                  version      = {v0.0.1},
+                  doi          = {10.5281/zenodo.5371628},
+                  url          = {https://doi.org/10.5281/zenodo.5371628}
+                }
+}""")
+
+st.markdown("""3. For style https://fossheim.io/writing/posts/css-text-gradient/""")
+st.markdown("""4. For style https://css-tricks.com/css-hover-effects-background-masks-3d/""")

requirements.txt

@@ -0,0 +1,4 @@
+streamlit==1.10.0
+torch
+transformers[sentencepiece]
+accelerate