app.py

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"))