lewtun's picture Fix timestamps 3f9b5a9
import os
from datetime import datetime
from pathlib import Path
import numpy as np
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():
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") == "raft" and tags.get("type") == "evaluation":
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"]
response = requests.get(
data = response.json()
card_data = data["cardData"]
username = card_data["submission_dataset"].split("/")[0]
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))
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"]
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.title("RAFT: Real-world Annotated Few-shot Tasks")
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
table td:nth-child(1) {
display: none
table th:nth-child(1) {
display: none