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| import inspect | |
| import os | |
| import uuid | |
| from pathlib import Path | |
| import pandas as pd | |
| import streamlit as st | |
| from datasets import get_dataset_config_names | |
| from dotenv import load_dotenv | |
| from evaluate import load | |
| from huggingface_hub import list_datasets, list_metrics | |
| from tqdm import tqdm | |
| from evaluation import filter_evaluated_models | |
| from utils import ( | |
| commit_evaluation_log, | |
| format_col_mapping, | |
| get_compatible_models, | |
| get_key, | |
| get_metadata, | |
| http_get, | |
| http_post, | |
| ) | |
| if Path(".env").is_file(): | |
| load_dotenv(".env") | |
| HF_TOKEN = os.getenv("HF_TOKEN") | |
| AUTOTRAIN_USERNAME = os.getenv("AUTOTRAIN_USERNAME") | |
| AUTOTRAIN_BACKEND_API = os.getenv("AUTOTRAIN_BACKEND_API") | |
| DATASETS_PREVIEW_API = os.getenv("DATASETS_PREVIEW_API") | |
| TASK_TO_ID = { | |
| "binary_classification": 1, | |
| "multi_class_classification": 2, | |
| # "multi_label_classification": 3, # Not fully supported in AutoTrain | |
| "entity_extraction": 4, | |
| "extractive_question_answering": 5, | |
| "translation": 6, | |
| "summarization": 8, | |
| } | |
| TASK_TO_DEFAULT_METRICS = { | |
| "binary_classification": ["f1", "precision", "recall", "auc", "accuracy"], | |
| "multi_class_classification": [ | |
| "f1", | |
| "precision", | |
| "recall", | |
| "accuracy", | |
| ], | |
| "entity_extraction": ["precision", "recall", "f1", "accuracy"], | |
| "extractive_question_answering": [], | |
| "translation": ["sacrebleu"], | |
| "summarization": ["rouge1", "rouge2", "rougeL", "rougeLsum"], | |
| } | |
| SUPPORTED_TASKS = list(TASK_TO_ID.keys()) | |
| def get_supported_metrics(): | |
| metrics = [metric.id for metric in list_metrics()] | |
| supported_metrics = [] | |
| for metric in tqdm(metrics): | |
| # TODO: this currently requires all metric dependencies to be installed | |
| # in the same environment. Refactor to avoid needing to actually load | |
| # the metric. | |
| try: | |
| metric_func = load(metric) | |
| except Exception as e: | |
| print(e) | |
| print("WARNING -- Skipping the following metric, which cannot load:", metric) | |
| continue | |
| argspec = inspect.getfullargspec(metric_func.compute) | |
| if "references" in argspec.kwonlyargs and "predictions" in argspec.kwonlyargs: | |
| # We require that "references" and "predictions" are arguments | |
| # to the metric function. We also require that the other arguments | |
| # besides "references" and "predictions" have defaults and so do not | |
| # need to be specified explicitly. | |
| defaults = True | |
| for key, value in argspec.kwonlydefaults.items(): | |
| if key not in ("references", "predictions"): | |
| if value is None: | |
| defaults = False | |
| break | |
| if defaults: | |
| supported_metrics.append(metric) | |
| return supported_metrics | |
| supported_metrics = get_supported_metrics() | |
| ####### | |
| # APP # | |
| ####### | |
| st.title("Evaluation on the Hub") | |
| st.markdown( | |
| """ | |
| Welcome to Hugging Face's automatic model evaluator! This application allows | |
| you to evaluate 🤗 Transformers | |
| [models](https://huggingface.co/models?library=transformers&sort=downloads) | |
| across a wide variety of datasets on the Hub -- all for free! Please select | |
| the dataset and configuration below. The results of your evaluation will be | |
| displayed on the [public | |
| leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards). | |
| """ | |
| ) | |
| all_datasets = [d.id for d in list_datasets()] | |
| query_params = st.experimental_get_query_params() | |
| default_dataset = all_datasets[0] | |
| if "dataset" in query_params: | |
| if len(query_params["dataset"]) > 0 and query_params["dataset"][0] in all_datasets: | |
| default_dataset = query_params["dataset"][0] | |
| selected_dataset = st.selectbox( | |
| "Select a dataset", | |
| all_datasets, | |
| index=all_datasets.index(default_dataset), | |
| help="""Datasets with metadata can be evaluated with 1-click. Check out the \ | |
| [documentation](https://huggingface.co/docs/hub/datasets-cards) to add \ | |
| evaluation metadata to a dataset.""", | |
| ) | |
| st.experimental_set_query_params(**{"dataset": [selected_dataset]}) | |
| metadata = get_metadata(selected_dataset) | |
| print(f"INFO -- Dataset metadata: {metadata}") | |
| if metadata is None: | |
| st.warning("No evaluation metadata found. Please configure the evaluation job below.") | |
| with st.expander("Advanced configuration"): | |
| # Select task | |
| selected_task = st.selectbox( | |
| "Select a task", | |
| SUPPORTED_TASKS, | |
| index=SUPPORTED_TASKS.index(metadata[0]["task_id"]) if metadata is not None else 0, | |
| ) | |
| # Select config | |
| configs = get_dataset_config_names(selected_dataset) | |
| selected_config = st.selectbox("Select a config", configs) | |
| # Select splits | |
| splits_resp = http_get( | |
| path="/splits", | |
| domain=DATASETS_PREVIEW_API, | |
| params={"dataset": selected_dataset}, | |
| ) | |
| if splits_resp.status_code == 200: | |
| split_names = [] | |
| all_splits = splits_resp.json() | |
| for split in all_splits["splits"]: | |
| if split["config"] == selected_config: | |
| split_names.append(split["split"]) | |
| if metadata is not None: | |
| eval_split = metadata[0]["splits"].get("eval_split", None) | |
| else: | |
| eval_split = None | |
| selected_split = st.selectbox( | |
| "Select a split", | |
| split_names, | |
| index=split_names.index(eval_split) if eval_split is not None else 0, | |
| ) | |
| # Select columns | |
| rows_resp = http_get( | |
| path="/rows", | |
| domain=DATASETS_PREVIEW_API, | |
| params={ | |
| "dataset": selected_dataset, | |
| "config": selected_config, | |
| "split": selected_split, | |
| }, | |
| ).json() | |
| col_names = list(pd.json_normalize(rows_resp["rows"][0]["row"]).columns) | |
| st.markdown("**Map your data columns**") | |
| col1, col2 = st.columns(2) | |
| # TODO: find a better way to layout these items | |
| # TODO: need graceful way of handling dataset <--> task mismatch for datasets with metadata | |
| col_mapping = {} | |
| if selected_task in ["binary_classification", "multi_class_classification"]: | |
| with col1: | |
| st.markdown("`text` column") | |
| st.text("") | |
| st.text("") | |
| st.text("") | |
| st.text("") | |
| st.markdown("`target` column") | |
| with col2: | |
| text_col = st.selectbox( | |
| "This column should contain the text you want to classify", | |
| col_names, | |
| index=col_names.index(get_key(metadata[0]["col_mapping"], "text")) if metadata is not None else 0, | |
| ) | |
| target_col = st.selectbox( | |
| "This column should contain the labels you want to assign to the text", | |
| col_names, | |
| index=col_names.index(get_key(metadata[0]["col_mapping"], "target")) if metadata is not None else 0, | |
| ) | |
| col_mapping[text_col] = "text" | |
| col_mapping[target_col] = "target" | |
| elif selected_task == "entity_extraction": | |
| with col1: | |
| st.markdown("`tokens` column") | |
| st.text("") | |
| st.text("") | |
| st.text("") | |
| st.text("") | |
| st.markdown("`tags` column") | |
| with col2: | |
| tokens_col = st.selectbox( | |
| "This column should contain the array of tokens", | |
| col_names, | |
| index=col_names.index(get_key(metadata[0]["col_mapping"], "tokens")) if metadata is not None else 0, | |
| ) | |
| tags_col = st.selectbox( | |
| "This column should contain the labels to associate to each part of the text", | |
| col_names, | |
| index=col_names.index(get_key(metadata[0]["col_mapping"], "tags")) if metadata is not None else 0, | |
| ) | |
| col_mapping[tokens_col] = "tokens" | |
| col_mapping[tags_col] = "tags" | |
| elif selected_task == "translation": | |
| with col1: | |
| st.markdown("`source` column") | |
| st.text("") | |
| st.text("") | |
| st.text("") | |
| st.text("") | |
| st.markdown("`target` column") | |
| with col2: | |
| text_col = st.selectbox( | |
| "This column should contain the text you want to translate", | |
| col_names, | |
| index=col_names.index(get_key(metadata[0]["col_mapping"], "source")) if metadata is not None else 0, | |
| ) | |
| target_col = st.selectbox( | |
| "This column should contain an example translation of the source text", | |
| col_names, | |
| index=col_names.index(get_key(metadata[0]["col_mapping"], "target")) if metadata is not None else 0, | |
| ) | |
| col_mapping[text_col] = "source" | |
| col_mapping[target_col] = "target" | |
| elif selected_task == "summarization": | |
| with col1: | |
| st.markdown("`text` column") | |
| st.text("") | |
| st.text("") | |
| st.text("") | |
| st.text("") | |
| st.markdown("`target` column") | |
| with col2: | |
| text_col = st.selectbox( | |
| "This column should contain the text you want to summarize", | |
| col_names, | |
| index=col_names.index(get_key(metadata[0]["col_mapping"], "text")) if metadata is not None else 0, | |
| ) | |
| target_col = st.selectbox( | |
| "This column should contain an example summarization of the text", | |
| col_names, | |
| index=col_names.index(get_key(metadata[0]["col_mapping"], "target")) if metadata is not None else 0, | |
| ) | |
| col_mapping[text_col] = "text" | |
| col_mapping[target_col] = "target" | |
| elif selected_task == "extractive_question_answering": | |
| if metadata is not None: | |
| col_mapping = metadata[0]["col_mapping"] | |
| # Hub YAML parser converts periods to hyphens, so we remap them here | |
| col_mapping = format_col_mapping(col_mapping) | |
| with col1: | |
| st.markdown("`context` column") | |
| st.text("") | |
| st.text("") | |
| st.text("") | |
| st.text("") | |
| st.markdown("`question` column") | |
| st.text("") | |
| st.text("") | |
| st.text("") | |
| st.text("") | |
| st.markdown("`answers.text` column") | |
| st.text("") | |
| st.text("") | |
| st.text("") | |
| st.text("") | |
| st.markdown("`answers.answer_start` column") | |
| with col2: | |
| context_col = st.selectbox( | |
| "This column should contain the question's context", | |
| col_names, | |
| index=col_names.index(get_key(col_mapping, "context")) if metadata is not None else 0, | |
| ) | |
| question_col = st.selectbox( | |
| "This column should contain the question to be answered, given the context", | |
| col_names, | |
| index=col_names.index(get_key(col_mapping, "question")) if metadata is not None else 0, | |
| ) | |
| answers_text_col = st.selectbox( | |
| "This column should contain example answers to the question, extracted from the context", | |
| col_names, | |
| index=col_names.index(get_key(col_mapping, "answers.text")) if metadata is not None else 0, | |
| ) | |
| answers_start_col = st.selectbox( | |
| "This column should contain the indices in the context of the first character of each answers.text", | |
| col_names, | |
| index=col_names.index(get_key(col_mapping, "answers.answer_start")) if metadata is not None else 0, | |
| ) | |
| col_mapping[context_col] = "context" | |
| col_mapping[question_col] = "question" | |
| col_mapping[answers_text_col] = "answers.text" | |
| col_mapping[answers_start_col] = "answers.answer_start" | |
| # Select metrics | |
| st.markdown("**Select metrics**") | |
| st.markdown("The following metrics will be computed") | |
| html_string = " ".join( | |
| [ | |
| '<div style="padding-right:5px;padding-left:5px;padding-top:5px;padding-bottom:5px;float:left">' | |
| + '<div style="background-color:#D3D3D3;border-radius:5px;display:inline-block;padding-right:5px;' | |
| + 'padding-left:5px;color:white">' | |
| + metric | |
| + "</div></div>" | |
| for metric in TASK_TO_DEFAULT_METRICS[selected_task] | |
| ] | |
| ) | |
| st.markdown(html_string, unsafe_allow_html=True) | |
| selected_metrics = st.multiselect( | |
| "(Optional) Select additional metrics", | |
| list(set(supported_metrics) - set(TASK_TO_DEFAULT_METRICS[selected_task])), | |
| ) | |
| st.info( | |
| """Note: user-selected metrics will be run with their default arguments. \ | |
| Check out the [available metrics](https://huggingface.co/metrics) for more details.""" | |
| ) | |
| with st.form(key="form"): | |
| compatible_models = get_compatible_models(selected_task, selected_dataset) | |
| selected_models = st.multiselect( | |
| "Select the models you wish to evaluate", | |
| compatible_models, | |
| help="""Don't see your model in this list? Add the dataset and task it was trained to the \ | |
| [model card metadata.](https://huggingface.co/docs/hub/models-cards#model-card-metadata)""", | |
| ) | |
| print("INFO -- Selected models before filter:", selected_models) | |
| if len(selected_models) > 0: | |
| selected_models = filter_evaluated_models( | |
| selected_models, | |
| selected_task, | |
| selected_dataset, | |
| selected_config, | |
| selected_split, | |
| ) | |
| print("INFO -- Selected models after filter:", selected_models) | |
| submit_button = st.form_submit_button("Evaluate models 🚀") | |
| if submit_button: | |
| if len(selected_models) > 0: | |
| project_id = str(uuid.uuid4())[:8] | |
| project_payload = { | |
| "username": AUTOTRAIN_USERNAME, | |
| "proj_name": f"eval-project-{project_id}", | |
| "task": TASK_TO_ID[selected_task], | |
| "config": { | |
| "language": "en" | |
| if selected_task != "translation" | |
| else "en2de", # Need this dummy pair to enable translation | |
| "max_models": 5, | |
| "instance": { | |
| "provider": "aws", | |
| "instance_type": "ml.g4dn.4xlarge", | |
| "max_runtime_seconds": 172800, | |
| "num_instances": 1, | |
| "disk_size_gb": 150, | |
| }, | |
| "evaluation": { | |
| "metrics": selected_metrics, | |
| "models": selected_models, | |
| }, | |
| }, | |
| } | |
| print(f"INFO -- Payload: {project_payload}") | |
| project_json_resp = http_post( | |
| path="/projects/create", | |
| payload=project_payload, | |
| token=HF_TOKEN, | |
| domain=AUTOTRAIN_BACKEND_API, | |
| ).json() | |
| print(f"INFO -- Project creation response: {project_json_resp}") | |
| if project_json_resp["created"]: | |
| data_payload = { | |
| "split": 4, # use "auto" split choice in AutoTrain | |
| "col_mapping": col_mapping, | |
| "load_config": {"max_size_bytes": 0, "shuffle": False}, | |
| } | |
| data_json_resp = http_post( | |
| path=f"/projects/{project_json_resp['id']}/data/{selected_dataset}", | |
| payload=data_payload, | |
| token=HF_TOKEN, | |
| domain=AUTOTRAIN_BACKEND_API, | |
| params={ | |
| "type": "dataset", | |
| "config_name": selected_config, | |
| "split_name": selected_split, | |
| }, | |
| ).json() | |
| print(f"INFO -- Dataset creation response: {data_json_resp}") | |
| if data_json_resp["download_status"] == 1: | |
| train_json_resp = http_get( | |
| path=f"/projects/{project_json_resp['id']}/data/start_process", | |
| token=HF_TOKEN, | |
| domain=AUTOTRAIN_BACKEND_API, | |
| ).json() | |
| print(f"INFO -- AutoTrain job response: {train_json_resp}") | |
| if train_json_resp["success"]: | |
| st.success(f"✅ Successfully submitted evaluation job with project name {project_id}") | |
| st.markdown( | |
| f""" | |
| Evaluation can take up to 1 hour to complete, so grab a ☕ or 🍵 while you wait: | |
| 📊 Click [here](https://hf.co/spaces/autoevaluate/leaderboards?dataset={selected_dataset}) \ | |
| to view the results from your submission | |
| """ | |
| ) | |
| print("INFO -- Pushing evaluation job logs to the Hub") | |
| evaluation_log = {} | |
| evaluation_log["payload"] = project_payload | |
| evaluation_log["project_creation_response"] = project_json_resp | |
| evaluation_log["dataset_creation_response"] = data_json_resp | |
| evaluation_log["autotrain_job_response"] = train_json_resp | |
| commit_evaluation_log(evaluation_log, hf_access_token=HF_TOKEN) | |
| else: | |
| st.error("🙈 Oh no, there was an error submitting your evaluation job!") | |
| else: | |
| st.warning("⚠️ No models were selected for evaluation!") | |