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import os |
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import uuid |
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from pathlib import Path |
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import pandas as pd |
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import streamlit as st |
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from datasets import get_dataset_config_names, list_metrics, load_metric |
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from dotenv import load_dotenv |
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from huggingface_hub import list_datasets |
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from tqdm import tqdm |
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import inspect |
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from evaluation import filter_evaluated_models |
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from utils import get_compatible_models, get_key, get_metadata, http_get, http_post |
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if Path(".env").is_file(): |
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load_dotenv(".env") |
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HF_TOKEN = os.getenv("HF_TOKEN") |
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AUTOTRAIN_USERNAME = os.getenv("AUTOTRAIN_USERNAME") |
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AUTOTRAIN_BACKEND_API = os.getenv("AUTOTRAIN_BACKEND_API") |
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DATASETS_PREVIEW_API = os.getenv("DATASETS_PREVIEW_API") |
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TASK_TO_ID = { |
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"binary_classification": 1, |
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"multi_class_classification": 2, |
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"entity_extraction": 4, |
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"extractive_question_answering": 5, |
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"summarization": 8, |
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} |
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TASK_TO_DEFAULT_METRICS = { |
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"binary_classification": ["f1", "precision", "recall", "auc", "accuracy"], |
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"multi_class_classification": ["f1_micro", "f1_macro", "f1_weighted", "precision_macro", "precision_micro", "precision_weighted", "recall_macro", "recall_micro", "recall_weighted", "accuracy"], |
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"entity_extraction": ["precision", "recall", "f1", "accuracy"], |
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"extractive_question_answering": [], |
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"translation": ["sacrebleu", "gen_len"], |
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"summarization": ["rouge1", "rouge2", "rougeL", "rougeLsum", "gen_len"], |
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} |
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SUPPORTED_TASKS = list(TASK_TO_ID.keys()) |
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@st.cache |
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def get_supported_metrics(): |
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metrics = list_metrics() |
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supported_metrics = [] |
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for metric in tqdm(metrics): |
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try: |
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metric_func = load_metric(metric) |
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except Exception as e: |
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print(e) |
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print("Skipping the following metric, which cannot load:", metric) |
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argspec = inspect.getfullargspec(metric_func.compute) |
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if ( |
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"references" in argspec.kwonlyargs |
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and "predictions" in argspec.kwonlyargs |
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): |
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defaults = True |
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for key, value in argspec.kwonlydefaults.items(): |
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if key not in ("references", "predictions"): |
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if value is None: |
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defaults = False |
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break |
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if defaults: |
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supported_metrics.append(metric) |
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return supported_metrics |
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supported_metrics = get_supported_metrics() |
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st.title("Evaluation as a Service") |
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st.markdown( |
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""" |
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Welcome to Hugging Face's Evaluation as a Service! This application allows |
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you to evaluate π€ Transformers models with a dataset on the Hub. Please |
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select the dataset and configuration below. The results of your evaluation |
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will be displayed on the public leaderboard |
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[here](https://huggingface.co/spaces/autoevaluate/leaderboards). |
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""" |
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) |
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all_datasets = [d.id for d in list_datasets()] |
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query_params = st.experimental_get_query_params() |
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default_dataset = all_datasets[0] |
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if "dataset" in query_params: |
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if len(query_params["dataset"]) > 0 and query_params["dataset"][0] in all_datasets: |
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default_dataset = query_params["dataset"][0] |
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selected_dataset = st.selectbox("Select a dataset", all_datasets, index=all_datasets.index(default_dataset)) |
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st.experimental_set_query_params(**{"dataset": [selected_dataset]}) |
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metadata = get_metadata(selected_dataset) |
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print(metadata) |
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if metadata is None: |
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st.warning("No evaluation metadata found. Please configure the evaluation job below.") |
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with st.expander("Advanced configuration"): |
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selected_task = st.selectbox( |
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"Select a task", |
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SUPPORTED_TASKS, |
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index=SUPPORTED_TASKS.index(metadata[0]["task_id"]) if metadata is not None else 0, |
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) |
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configs = get_dataset_config_names(selected_dataset) |
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selected_config = st.selectbox("Select a config", configs) |
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splits_resp = http_get( |
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path="/splits", |
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domain=DATASETS_PREVIEW_API, |
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params={"dataset": selected_dataset}, |
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) |
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if splits_resp.status_code == 200: |
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split_names = [] |
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all_splits = splits_resp.json() |
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for split in all_splits["splits"]: |
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if split["config"] == selected_config: |
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split_names.append(split["split"]) |
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selected_split = st.selectbox( |
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"Select a split", |
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split_names, |
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index=split_names.index(metadata[0]["splits"]["eval_split"]) if metadata is not None else 0, |
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) |
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rows_resp = http_get( |
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path="/rows", |
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domain=DATASETS_PREVIEW_API, |
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params={ |
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"dataset": selected_dataset, |
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"config": selected_config, |
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"split": selected_split, |
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}, |
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).json() |
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col_names = list(pd.json_normalize(rows_resp["rows"][0]["row"]).columns) |
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st.markdown("**Map your data columns**") |
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col1, col2 = st.columns(2) |
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col_mapping = {} |
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if selected_task in ["binary_classification", "multi_class_classification"]: |
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with col1: |
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st.markdown("`text` column") |
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st.text("") |
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st.text("") |
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st.text("") |
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st.text("") |
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st.markdown("`target` column") |
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with col2: |
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text_col = st.selectbox( |
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"This column should contain the text you want to classify", |
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col_names, |
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index=col_names.index(get_key(metadata[0]["col_mapping"], "text")) if metadata is not None else 0, |
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) |
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target_col = st.selectbox( |
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"This column should contain the labels you want to assign to the text", |
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col_names, |
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index=col_names.index(get_key(metadata[0]["col_mapping"], "target")) if metadata is not None else 0, |
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) |
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col_mapping[text_col] = "text" |
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col_mapping[target_col] = "target" |
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elif selected_task == "entity_extraction": |
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with col1: |
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st.markdown("`tokens` column") |
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st.text("") |
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st.text("") |
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st.text("") |
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st.text("") |
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st.markdown("`tags` column") |
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with col2: |
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tokens_col = st.selectbox( |
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"This column should contain the array of tokens", |
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col_names, |
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index=col_names.index(get_key(metadata[0]["col_mapping"], "tokens")) if metadata is not None else 0, |
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) |
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tags_col = st.selectbox( |
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"This column should contain the labels to associate to each part of the text", |
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col_names, |
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index=col_names.index(get_key(metadata[0]["col_mapping"], "tags")) if metadata is not None else 0, |
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) |
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col_mapping[tokens_col] = "tokens" |
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col_mapping[tags_col] = "tags" |
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elif selected_task == "translation": |
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with col1: |
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st.markdown("`source` column") |
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st.text("") |
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st.text("") |
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st.text("") |
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st.text("") |
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st.markdown("`target` column") |
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with col2: |
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text_col = st.selectbox( |
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"This column should contain the text you want to translate", |
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col_names, |
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index=col_names.index(get_key(metadata[0]["col_mapping"], "source")) if metadata is not None else 0, |
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) |
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target_col = st.selectbox( |
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"This column should contain an example translation of the source text", |
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col_names, |
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index=col_names.index(get_key(metadata[0]["col_mapping"], "target")) if metadata is not None else 0, |
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) |
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col_mapping[text_col] = "source" |
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col_mapping[target_col] = "target" |
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elif selected_task == "summarization": |
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with col1: |
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st.markdown("`text` column") |
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st.text("") |
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st.text("") |
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st.text("") |
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st.text("") |
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st.markdown("`target` column") |
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with col2: |
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text_col = st.selectbox( |
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"This column should contain the text you want to summarize", |
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col_names, |
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index=col_names.index(get_key(metadata[0]["col_mapping"], "text")) if metadata is not None else 0, |
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) |
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target_col = st.selectbox( |
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"This column should contain an example summarization of the text", |
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col_names, |
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index=col_names.index(get_key(metadata[0]["col_mapping"], "target")) if metadata is not None else 0, |
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) |
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col_mapping[text_col] = "text" |
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col_mapping[target_col] = "target" |
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elif selected_task == "extractive_question_answering": |
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col_mapping = metadata[0]["col_mapping"] |
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col_mapping = {k.replace("-", "."): v.replace("-", ".") for k, v in col_mapping.items()} |
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with col1: |
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st.markdown("`context` column") |
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st.text("") |
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st.text("") |
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st.text("") |
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st.text("") |
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st.markdown("`question` column") |
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st.text("") |
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st.text("") |
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st.text("") |
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st.text("") |
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st.markdown("`answers.text` column") |
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st.text("") |
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st.text("") |
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st.text("") |
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st.text("") |
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st.markdown("`answers.answer_start` column") |
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with col2: |
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context_col = st.selectbox( |
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"This column should contain the question's context", |
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col_names, |
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index=col_names.index(get_key(col_mapping, "context")) if metadata is not None else 0, |
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) |
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question_col = st.selectbox( |
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"This column should contain the question to be answered, given the context", |
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col_names, |
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index=col_names.index(get_key(col_mapping, "question")) if metadata is not None else 0, |
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) |
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answers_text_col = st.selectbox( |
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"This column should contain example answers to the question, extracted from the context", |
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col_names, |
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index=col_names.index(get_key(col_mapping, "answers.text")) if metadata is not None else 0, |
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) |
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answers_start_col = st.selectbox( |
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"This column should contain the indices in the context of the first character of each answers.text", |
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col_names, |
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index=col_names.index(get_key(col_mapping, "answers.answer_start")) if metadata is not None else 0, |
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) |
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col_mapping[context_col] = "context" |
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col_mapping[question_col] = "question" |
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col_mapping[answers_text_col] = "answers.text" |
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col_mapping[answers_start_col] = "answers.answer_start" |
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with st.form(key="form"): |
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compatible_models = get_compatible_models(selected_task, selected_dataset) |
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st.markdown("The following metrics will be computed") |
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html_string = " ".join([ |
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"<div style=\"padding-right:5px;padding-left:5px;padding-top:5px;padding-bottom:5px;float:left\">" |
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+ "<div style=\"background-color:#D3D3D3;border-radius:5px;display:inline-block;padding-right:5px;padding-left:5px;color:white\">" |
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+ metric + "</div></div>" for metric in TASK_TO_DEFAULT_METRICS[selected_task] |
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]) |
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st.markdown(html_string, unsafe_allow_html=True) |
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selected_metrics = st.multiselect( |
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"(Optional) Select additional metrics", |
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list(set(supported_metrics) - set(TASK_TO_DEFAULT_METRICS[selected_task])), |
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) |
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st.info("Note: user-selected metrics will be run with their default arguments from [here](https://github.com/huggingface/datasets/tree/master/metrics)") |
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selected_models = st.multiselect("Select the models you wish to evaluate", compatible_models) |
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print("Selected models:", selected_models) |
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selected_models = filter_evaluated_models( |
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selected_models, |
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selected_task, |
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selected_dataset, |
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selected_config, |
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selected_split, |
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) |
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print("Selected models:", selected_models) |
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submit_button = st.form_submit_button("Make submission") |
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if submit_button: |
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if len(selected_models) > 0: |
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project_id = str(uuid.uuid4())[:3] |
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payload = { |
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"username": AUTOTRAIN_USERNAME, |
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"proj_name": f"my-eval-project-{project_id}", |
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"task": TASK_TO_ID[selected_task], |
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"config": { |
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"language": "en", |
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"max_models": 5, |
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"instance": { |
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"provider": "aws", |
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"instance_type": "ml.g4dn.4xlarge", |
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"max_runtime_seconds": 172800, |
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"num_instances": 1, |
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"disk_size_gb": 150, |
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}, |
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"evaluation": { |
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"metrics": selected_metrics, |
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"models": selected_models, |
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}, |
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}, |
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} |
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print(f"Payload: {payload}") |
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project_json_resp = http_post( |
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path="/projects/create", |
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payload=payload, |
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token=HF_TOKEN, |
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domain=AUTOTRAIN_BACKEND_API, |
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).json() |
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print(project_json_resp) |
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if project_json_resp["created"]: |
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payload = { |
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"split": 4, |
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"col_mapping": col_mapping, |
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"load_config": {"max_size_bytes": 0, "shuffle": False}, |
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} |
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data_json_resp = http_post( |
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path=f"/projects/{project_json_resp['id']}/data/{selected_dataset}", |
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payload=payload, |
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token=HF_TOKEN, |
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domain=AUTOTRAIN_BACKEND_API, |
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params={ |
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"type": "dataset", |
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"config_name": selected_config, |
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"split_name": selected_split, |
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}, |
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).json() |
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print(data_json_resp) |
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if data_json_resp["download_status"] == 1: |
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train_json_resp = http_get( |
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path=f"/projects/{project_json_resp['id']}/data/start_process", |
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token=HF_TOKEN, |
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domain=AUTOTRAIN_BACKEND_API, |
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).json() |
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print(train_json_resp) |
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if train_json_resp["success"]: |
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st.success(f"β
Successfully submitted evaluation job with project ID {project_id}") |
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st.markdown( |
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f""" |
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Evaluation takes appoximately 1 hour to complete, so grab a β or π΅ while you wait: |
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π Click [here](https://hf.co/spaces/autoevaluate/leaderboards?dataset={selected_dataset}) \ |
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to view the results from your submission |
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""" |
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) |
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else: |
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st.error("π Oh noes, there was an error submitting your evaluation job!") |
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else: |
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st.warning("β οΈ No models were selected for evaluation!") |
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