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| import os | |
| from datetime import datetime | |
| from pathlib import Path | |
| from re import sub | |
| import pandas as pd | |
| import numpy as np | |
| import requests | |
| import streamlit as st | |
| from datasets import get_dataset_config_names | |
| from dotenv import load_dotenv | |
| if Path(".env").is_file(): | |
| load_dotenv(".env") | |
| auth_token = os.getenv("HF_HUB_TOKEN") | |
| header = {"Authorization": "Bearer " + auth_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(): | |
| response = requests.get("http://huggingface.co/api/datasets", headers=header) | |
| all_datasets = response.json() | |
| submissions = [] | |
| for dataset in all_datasets: | |
| tags = extract_tags(dataset) | |
| if tags.get("benchmark") == "ought/raft" and tags.get("type") == "evaluation": | |
| submissions.append(dataset) | |
| return submissions | |
| def format_submissions(submissions): | |
| submission_data = {**{"Submission": []}, **{"Date": []}, **{t: [] for t in TASKS}} | |
| # TODO(lewtun): delete / filter all the junk repos from development | |
| # The following picks the latest submissions which adhere to the model card schema | |
| for submission in submissions: | |
| submission_id = submission["id"] | |
| response = requests.get( | |
| f"http://huggingface.co/api/datasets/{submission_id}?full=true", | |
| headers=header, | |
| ) | |
| data = response.json() | |
| card_data = data["card_data"] | |
| submission_name = card_data["submission_dataset"] | |
| submission_data["Submission"].append(submission_name) | |
| submission_id = card_data["submission_id"] | |
| timestamp = submission_id.split("-")[-1] | |
| timestamp = pd.to_datetime(int(timestamp)) | |
| submission_data["Date"].append(datetime.date(timestamp)) | |
| 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(2, "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://github.com/oughtinc/raft_submission). | |
| """ | |
| ) | |
| submissions = download_submissions() | |
| df = format_submissions(submissions) | |
| # hack to remove index column from https://github.com/streamlit/streamlit/issues/641 | |
| # st.table(df.assign(hack="").set_index("hack").style.format(precision=3)) | |
| st.table(df.assign(hack="").set_index("hack")) | |