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import os
from datetime import datetime
from pathlib import Path
from re import sub
import pandas as pd
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 = get_dataset_config_names("ought/raft")
# Split and capitalize the task names, e.g. banking_77 => Banking 77
FORMATTED_TASK_NAMES = [" ".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).reset_index().rename(columns={"index": "Rank"})
df.rename(columns={k: v for k, v in zip(TASKS, FORMATTED_TASK_NAMES)}, inplace=True)
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"))
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