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| import os | |
| from datetime import datetime | |
| from pathlib import Path | |
| import numpy as np | |
| import pandas as pd | |
| import streamlit as st | |
| from datasets import get_dataset_config_names | |
| from dotenv import load_dotenv | |
| from huggingface_hub import DatasetFilter, list_datasets | |
| if Path(".env").is_file(): | |
| load_dotenv(".env") | |
| auth_token = os.getenv("HF_HUB_TOKEN") | |
| TASKS = sorted(get_dataset_config_names("ought/raft")) | |
| # Split and capitalize the task names, e.g. banking_77 => Banking 77 | |
| FORMATTED_TASK_NAMES = sorted([" ".join(t.capitalize() for t in task.split("_")) for task in TASKS]) | |
| def extract_tags(dataset): | |
| tags = {} | |
| for tag in dataset.tags: | |
| k, v = tuple(tag.split(":", 1)) | |
| tags[k] = v | |
| return tags | |
| def download_submissions(): | |
| filt = DatasetFilter(benchmark="raft") | |
| all_submissions = list_datasets(filter=filt, cardData=True, use_auth_token=auth_token) | |
| submissions = [] | |
| for dataset in all_submissions: | |
| tags = extract_tags(dataset) | |
| if tags.get("type") == "evaluation": | |
| submissions.append(dataset) | |
| return submissions | |
| def format_submissions(submissions): | |
| submission_data = { | |
| **{"Submitter": []}, | |
| **{"Submission Name": []}, | |
| **{"Submission Date": []}, | |
| **{t: [] for t in TASKS}, | |
| } | |
| # The following picks the latest submissions which adhere to the model card schema | |
| for submission in submissions: | |
| submission_id = submission.id | |
| card_data = submission.cardData | |
| username = card_data["submission_dataset"].split("/")[0] | |
| submission_data["Submitter"].append(username) | |
| submission_id = card_data["submission_id"] | |
| submission_name, sha, timestamp = submission_id.split("__") | |
| # Format submission names with new backend constraints | |
| # TODO(lewtun): make this less hacky! | |
| if "_XXX_" in submission_name: | |
| submission_name = submission_name.replace("_XXX_", " ") | |
| submission_data["Submission Name"].append(submission_name) | |
| # Handle mismatch in epoch microseconds vs epoch seconds in new AutoTrain API | |
| if len(timestamp) > 10: | |
| timestamp = pd.to_datetime(int(timestamp)) | |
| else: | |
| timestamp = pd.to_datetime(int(timestamp), unit="s") | |
| submission_data["Submission Date"].append(datetime.date(timestamp).strftime("%b %d, %Y")) | |
| for task in card_data["results"]: | |
| task_data = task["task"] | |
| task_name = task_data["name"] | |
| score = task_data["metrics"][0]["value"] | |
| submission_data[task_name].append(score) | |
| df = pd.DataFrame(submission_data) | |
| df.insert(3, "Overall", df[TASKS].mean(axis=1)) | |
| df = df.copy().sort_values("Overall", ascending=False) | |
| df.rename(columns={k: v for k, v in zip(TASKS, FORMATTED_TASK_NAMES)}, inplace=True) | |
| # Start ranking from 1 | |
| df.insert(0, "Rank", np.arange(1, len(df) + 1)) | |
| return df | |
| ########### | |
| ### APP ### | |
| ########### | |
| st.set_page_config(layout="wide") | |
| st.title("RAFT: Real-world Annotated Few-shot Tasks") | |
| st.markdown( | |
| """ | |
| Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples. Will models soon solve classification tasks that have so far been reserved for human research assistants? | |
| [RAFT](https://raft.elicit.org) is a few-shot classification benchmark that tests language models: | |
| - across multiple domains (lit review, tweets, customer interaction, etc.) | |
| - on economically valuable classification tasks (someone inherently cares about the task) | |
| - in a setting that mirrors deployment (50 examples per task, info retrieval allowed, hidden test set) | |
| To submit to RAFT, follow the instruction posted on [this page](https://huggingface.co/datasets/ought/raft-submission). | |
| """ | |
| ) | |
| submissions = download_submissions() | |
| df = format_submissions(submissions) | |
| styler = df.style.set_precision(3).set_properties(**{"white-space": "pre-wrap", "text-align": "center"}) | |
| # hack to remove index column: https://discuss.streamlit.io/t/questions-on-st-table/6878/3 | |
| st.markdown( | |
| """ | |
| <style> | |
| table td:nth-child(1) { | |
| display: none | |
| } | |
| table th:nth-child(1) { | |
| display: none | |
| } | |
| </style> | |
| """, | |
| unsafe_allow_html=True, | |
| ) | |
| st.table(styler) | |