Spaces:
Running
Running
| import os | |
| import time | |
| from pathlib import Path | |
| import pandas as pd | |
| import streamlit as st | |
| import yaml | |
| from datasets import get_dataset_config_names | |
| from dotenv import load_dotenv | |
| from huggingface_hub import list_datasets | |
| from evaluation import filter_evaluated_models | |
| from utils import ( | |
| AUTOTRAIN_TASK_TO_HUB_TASK, | |
| commit_evaluation_log, | |
| create_autotrain_project_name, | |
| format_col_mapping, | |
| get_compatible_models, | |
| get_config_metadata, | |
| get_dataset_card_url, | |
| 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") | |
| # Put image tasks on top | |
| TASK_TO_ID = { | |
| "image_binary_classification": 17, | |
| "image_multi_class_classification": 18, | |
| "binary_classification": 1, | |
| "multi_class_classification": 2, | |
| "natural_language_inference": 22, | |
| "entity_extraction": 4, | |
| "extractive_question_answering": 5, | |
| "translation": 6, | |
| "summarization": 8, | |
| "text_zero_shot_classification": 23, | |
| } | |
| TASK_TO_DEFAULT_METRICS = { | |
| "binary_classification": ["f1", "precision", "recall", "auc", "accuracy"], | |
| "multi_class_classification": [ | |
| "f1", | |
| "precision", | |
| "recall", | |
| "accuracy", | |
| ], | |
| "natural_language_inference": ["f1", "precision", "recall", "auc", "accuracy"], | |
| "entity_extraction": ["precision", "recall", "f1", "accuracy"], | |
| "extractive_question_answering": ["f1", "exact_match"], | |
| "translation": ["sacrebleu"], | |
| "summarization": ["rouge1", "rouge2", "rougeL", "rougeLsum"], | |
| "image_binary_classification": ["f1", "precision", "recall", "auc", "accuracy"], | |
| "image_multi_class_classification": [ | |
| "f1", | |
| "precision", | |
| "recall", | |
| "accuracy", | |
| ], | |
| "text_zero_shot_classification": ["accuracy", "loss"], | |
| } | |
| AUTOTRAIN_TASK_TO_LANG = { | |
| "translation": "en2de", | |
| "image_binary_classification": "unk", | |
| "image_multi_class_classification": "unk", | |
| } | |
| AUTOTRAIN_MACHINE = {"text_zero_shot_classification": "r5.16x"} | |
| SUPPORTED_TASKS = list(TASK_TO_ID.keys()) | |
| # Extracted from utils.get_supported_metrics | |
| # Hardcoded for now due to speed / caching constraints | |
| SUPPORTED_METRICS = [ | |
| "accuracy", | |
| "bertscore", | |
| "bleu", | |
| "cer", | |
| "chrf", | |
| "code_eval", | |
| "comet", | |
| "competition_math", | |
| "coval", | |
| "cuad", | |
| "exact_match", | |
| "f1", | |
| "frugalscore", | |
| "google_bleu", | |
| "mae", | |
| "mahalanobis", | |
| "matthews_correlation", | |
| "mean_iou", | |
| "meteor", | |
| "mse", | |
| "pearsonr", | |
| "perplexity", | |
| "precision", | |
| "recall", | |
| "roc_auc", | |
| "rouge", | |
| "sacrebleu", | |
| "sari", | |
| "seqeval", | |
| "spearmanr", | |
| "squad", | |
| "squad_v2", | |
| "ter", | |
| "trec_eval", | |
| "wer", | |
| "wiki_split", | |
| "xnli", | |
| "angelina-wang/directional_bias_amplification", | |
| "jordyvl/ece", | |
| "lvwerra/ai4code", | |
| "lvwerra/amex", | |
| ] | |
| ####### | |
| # 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](https://huggingface.co/datasets) on the | |
| Hub. Please select the dataset and configuration below. The results of your | |
| evaluation will be displayed on the [public | |
| leaderboards](https://huggingface.co/spaces/autoevaluate/leaderboards). For | |
| more details, check out out our [blog | |
| post](https://huggingface.co/blog/eval-on-the-hub). | |
| """ | |
| ) | |
| all_datasets = [d.id for d in list_datasets()] | |
| query_params = st.experimental_get_query_params() | |
| if "first_query_params" not in st.session_state: | |
| st.session_state.first_query_params = query_params | |
| first_query_params = st.session_state.first_query_params | |
| default_dataset = all_datasets[0] | |
| if "dataset" in first_query_params: | |
| if len(first_query_params["dataset"]) > 0 and first_query_params["dataset"][0] in all_datasets: | |
| default_dataset = first_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. Configure an evaluation job to add \ | |
| new metadata to a dataset card.""", | |
| ) | |
| st.experimental_set_query_params(**{"dataset": [selected_dataset]}) | |
| # Check if selected dataset can be streamed | |
| is_valid_dataset = http_get( | |
| path="/is-valid", | |
| domain=DATASETS_PREVIEW_API, | |
| params={"dataset": selected_dataset}, | |
| ).json() | |
| if is_valid_dataset["valid"] is False: | |
| st.error( | |
| """The dataset you selected is not currently supported. Open a \ | |
| [discussion](https://huggingface.co/spaces/autoevaluate/model-evaluator/discussions) for support.""" | |
| ) | |
| metadata = get_metadata(selected_dataset, token=HF_TOKEN) | |
| 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, | |
| help="""Don't see your favourite task here? Open a \ | |
| [discussion](https://huggingface.co/spaces/autoevaluate/model-evaluator/discussions) to request it!""", | |
| ) | |
| # Select config | |
| configs = get_dataset_config_names(selected_dataset) | |
| selected_config = st.selectbox( | |
| "Select a config", | |
| configs, | |
| help="""Some datasets contain several sub-datasets, known as _configurations_. \ | |
| Select one to evaluate your models on. \ | |
| See the [docs](https://huggingface.co/docs/datasets/master/en/load_hub#configurations) for more details. | |
| """, | |
| ) | |
| # Some datasets have multiple metadata (one per config), so we grab the one associated with the selected config | |
| config_metadata = get_config_metadata(selected_config, metadata) | |
| print(f"INFO -- Config metadata: {config_metadata}") | |
| # 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 config_metadata is not None: | |
| eval_split = config_metadata["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, | |
| help="Be wary when evaluating models on the `train` split.", | |
| ) | |
| # Select columns | |
| rows_resp = http_get( | |
| path="/first-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 dataset columns**") | |
| st.markdown( | |
| """The model evaluator uses a standardised set of column names for the input examples and labels. \ | |
| Please define the mapping between your dataset columns (right) and the standardised column names (left).""" | |
| ) | |
| 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 to be classified", | |
| col_names, | |
| index=col_names.index(get_key(config_metadata["col_mapping"], "text")) | |
| if config_metadata is not None | |
| else 0, | |
| ) | |
| target_col = st.selectbox( | |
| "This column should contain the labels associated with the text", | |
| col_names, | |
| index=col_names.index(get_key(config_metadata["col_mapping"], "target")) | |
| if config_metadata is not None | |
| else 0, | |
| ) | |
| col_mapping[text_col] = "text" | |
| col_mapping[target_col] = "target" | |
| elif selected_task == "text_zero_shot_classification": | |
| with col1: | |
| st.markdown("`text` column") | |
| st.text("") | |
| st.text("") | |
| st.text("") | |
| st.text("") | |
| st.markdown("`classes` 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 to be classified", | |
| col_names, | |
| index=col_names.index(get_key(config_metadata["col_mapping"], "text")) | |
| if config_metadata is not None | |
| else 0, | |
| ) | |
| classes_col = st.selectbox( | |
| "This column should contain the classes associated with the text", | |
| col_names, | |
| index=col_names.index(get_key(config_metadata["col_mapping"], "classes")) | |
| if config_metadata is not None | |
| else 0, | |
| ) | |
| target_col = st.selectbox( | |
| "This column should contain the index of the correct class", | |
| col_names, | |
| index=col_names.index(get_key(config_metadata["col_mapping"], "target")) | |
| if config_metadata is not None | |
| else 0, | |
| ) | |
| col_mapping[text_col] = "text" | |
| col_mapping[classes_col] = "classes" | |
| col_mapping[target_col] = "target" | |
| if selected_task in ["natural_language_inference"]: | |
| config_metadata = get_config_metadata(selected_config, metadata) | |
| with col1: | |
| st.markdown("`text1` column") | |
| st.text("") | |
| st.text("") | |
| st.text("") | |
| st.text("") | |
| st.text("") | |
| st.markdown("`text2` column") | |
| st.text("") | |
| st.text("") | |
| st.text("") | |
| st.text("") | |
| st.text("") | |
| st.markdown("`target` column") | |
| with col2: | |
| text1_col = st.selectbox( | |
| "This column should contain the first text passage to be classified", | |
| col_names, | |
| index=col_names.index(get_key(config_metadata["col_mapping"], "text1")) | |
| if config_metadata is not None | |
| else 0, | |
| ) | |
| text2_col = st.selectbox( | |
| "This column should contain the second text passage to be classified", | |
| col_names, | |
| index=col_names.index(get_key(config_metadata["col_mapping"], "text2")) | |
| if config_metadata is not None | |
| else 0, | |
| ) | |
| target_col = st.selectbox( | |
| "This column should contain the labels associated with the text", | |
| col_names, | |
| index=col_names.index(get_key(config_metadata["col_mapping"], "target")) | |
| if config_metadata is not None | |
| else 0, | |
| ) | |
| col_mapping[text1_col] = "text1" | |
| col_mapping[text2_col] = "text2" | |
| 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 to be classified", | |
| col_names, | |
| index=col_names.index(get_key(config_metadata["col_mapping"], "tokens")) | |
| if config_metadata is not None | |
| else 0, | |
| ) | |
| tags_col = st.selectbox( | |
| "This column should contain the labels associated with each part of the text", | |
| col_names, | |
| index=col_names.index(get_key(config_metadata["col_mapping"], "tags")) | |
| if config_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 to be translated", | |
| col_names, | |
| index=col_names.index(get_key(config_metadata["col_mapping"], "source")) | |
| if config_metadata is not None | |
| else 0, | |
| ) | |
| target_col = st.selectbox( | |
| "This column should contain the target translation", | |
| col_names, | |
| index=col_names.index(get_key(config_metadata["col_mapping"], "target")) | |
| if config_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 to be summarized", | |
| col_names, | |
| index=col_names.index(get_key(config_metadata["col_mapping"], "text")) | |
| if config_metadata is not None | |
| else 0, | |
| ) | |
| target_col = st.selectbox( | |
| "This column should contain the target summary", | |
| col_names, | |
| index=col_names.index(get_key(config_metadata["col_mapping"], "target")) | |
| if config_metadata is not None | |
| else 0, | |
| ) | |
| col_mapping[text_col] = "text" | |
| col_mapping[target_col] = "target" | |
| elif selected_task == "extractive_question_answering": | |
| if config_metadata is not None: | |
| col_mapping = config_metadata["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 config_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 config_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 config_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 config_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" | |
| elif selected_task in ["image_binary_classification", "image_multi_class_classification"]: | |
| with col1: | |
| st.markdown("`image` column") | |
| st.text("") | |
| st.text("") | |
| st.text("") | |
| st.text("") | |
| st.markdown("`target` column") | |
| with col2: | |
| image_col = st.selectbox( | |
| "This column should contain the images to be classified", | |
| col_names, | |
| index=col_names.index(get_key(config_metadata["col_mapping"], "image")) | |
| if config_metadata is not None | |
| else 0, | |
| ) | |
| target_col = st.selectbox( | |
| "This column should contain the labels associated with the images", | |
| col_names, | |
| index=col_names.index(get_key(config_metadata["col_mapping"], "target")) | |
| if config_metadata is not None | |
| else 0, | |
| ) | |
| col_mapping[image_col] = "image" | |
| col_mapping[target_col] = "target" | |
| # 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", | |
| sorted(list(set(SUPPORTED_METRICS) - set(TASK_TO_DEFAULT_METRICS[selected_task]))), | |
| help="""User-selected metrics will be computed with their default arguments. \ | |
| For example, `f1` will report results for binary labels. \ | |
| 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 favourite model in this list? Add the dataset and task it was trained on to the \ | |
| [model card metadata.](https://huggingface.co/docs/hub/models-cards#model-card-metadata)""", | |
| ) | |
| print("INFO -- Selected models before filter:", selected_models) | |
| hf_username = st.text_input("Enter your 🤗 Hub username to be notified when the evaluation is finished") | |
| submit_button = st.form_submit_button("Evaluate models 🚀") | |
| if submit_button: | |
| if len(hf_username) == 0: | |
| st.warning("No 🤗 Hub username provided! Please enter your username and try again.") | |
| elif len(selected_models) == 0: | |
| st.warning("⚠️ No models were selected for evaluation! Please select at least one model and try again.") | |
| elif len(selected_models) > 10: | |
| st.warning("Only 10 models can be evaluated at once. Please select fewer models and try again.") | |
| else: | |
| # Filter out previously evaluated models | |
| selected_models = filter_evaluated_models( | |
| selected_models, | |
| selected_task, | |
| selected_dataset, | |
| selected_config, | |
| selected_split, | |
| selected_metrics, | |
| ) | |
| print("INFO -- Selected models after filter:", selected_models) | |
| if len(selected_models) > 0: | |
| project_payload = { | |
| "username": AUTOTRAIN_USERNAME, | |
| "proj_name": create_autotrain_project_name(selected_dataset, selected_config), | |
| "task": TASK_TO_ID[selected_task], | |
| "config": { | |
| "language": AUTOTRAIN_TASK_TO_LANG[selected_task] | |
| if selected_task in AUTOTRAIN_TASK_TO_LANG | |
| else "en", | |
| "max_models": 5, | |
| "instance": { | |
| "provider": "sagemaker" if selected_task in AUTOTRAIN_MACHINE.keys() else "ovh", | |
| "instance_type": AUTOTRAIN_MACHINE[selected_task] | |
| if selected_task in AUTOTRAIN_MACHINE.keys() | |
| else "p3", | |
| "max_runtime_seconds": 172800, | |
| "num_instances": 1, | |
| "disk_size_gb": 150, | |
| }, | |
| "evaluation": { | |
| "metrics": selected_metrics, | |
| "models": selected_models, | |
| "hf_username": hf_username, | |
| }, | |
| }, | |
| } | |
| 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}, | |
| "dataset_id": selected_dataset, | |
| "dataset_config": selected_config, | |
| "dataset_split": selected_split, | |
| } | |
| data_json_resp = http_post( | |
| path=f"/projects/{project_json_resp['id']}/data/dataset", | |
| payload=data_payload, | |
| token=HF_TOKEN, | |
| domain=AUTOTRAIN_BACKEND_API, | |
| ).json() | |
| print(f"INFO -- Dataset creation response: {data_json_resp}") | |
| if data_json_resp["download_status"] == 1: | |
| train_json_resp = http_post( | |
| path=f"/projects/{project_json_resp['id']}/data/start_processing", | |
| token=HF_TOKEN, | |
| domain=AUTOTRAIN_BACKEND_API, | |
| ).json() | |
| # For local development we process and approve projects on-the-fly | |
| if "localhost" in AUTOTRAIN_BACKEND_API: | |
| with st.spinner("⏳ Waiting for data processing to complete ..."): | |
| is_data_processing_success = False | |
| while is_data_processing_success is not True: | |
| project_status = http_get( | |
| path=f"/projects/{project_json_resp['id']}", | |
| token=HF_TOKEN, | |
| domain=AUTOTRAIN_BACKEND_API, | |
| ).json() | |
| if project_status["status"] == 3: | |
| is_data_processing_success = True | |
| time.sleep(10) | |
| # Approve training job | |
| train_job_resp = http_post( | |
| path=f"/projects/{project_json_resp['id']}/start_training", | |
| token=HF_TOKEN, | |
| domain=AUTOTRAIN_BACKEND_API, | |
| ).json() | |
| st.success("✅ Data processing and project approval complete - go forth and evaluate!") | |
| else: | |
| # Prod/staging submissions are evaluated in a cron job via run_evaluation_jobs.py | |
| print(f"INFO -- AutoTrain job response: {train_json_resp}") | |
| if train_json_resp["success"]: | |
| train_eval_index = { | |
| "train-eval-index": [ | |
| { | |
| "config": selected_config, | |
| "task": AUTOTRAIN_TASK_TO_HUB_TASK[selected_task], | |
| "task_id": selected_task, | |
| "splits": {"eval_split": selected_split}, | |
| "col_mapping": col_mapping, | |
| } | |
| ] | |
| } | |
| selected_metadata = yaml.dump(train_eval_index, sort_keys=False) | |
| dataset_card_url = get_dataset_card_url(selected_dataset) | |
| st.success("✅ Successfully submitted evaluation job!") | |
| st.markdown( | |
| f""" | |
| Evaluation can take up to 1 hour to complete, so grab a ☕️ or 🍵 while you wait: | |
| * 🔔 A [Hub pull request](https://huggingface.co/docs/hub/repositories-pull-requests-discussions) with the evaluation results will be opened for each model you selected. Check your email for notifications. | |
| * 📊 Click [here](https://hf.co/spaces/autoevaluate/leaderboards?dataset={selected_dataset}) to view the results from your submission once the Hub pull request is merged. | |
| * 🥱 Tired of configuring evaluations? Add the following metadata to the [dataset card]({dataset_card_url}) to enable 1-click evaluations: | |
| """ # noqa | |
| ) | |
| st.markdown( | |
| f""" | |
| ```yaml | |
| {selected_metadata} | |
| """ | |
| ) | |
| print("INFO -- Pushing evaluation job logs to the Hub") | |
| evaluation_log = {} | |
| evaluation_log["project_id"] = project_json_resp["id"] | |
| evaluation_log["autotrain_env"] = ( | |
| "staging" if "staging" in AUTOTRAIN_BACKEND_API else "prod" | |
| ) | |
| 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 left to evaluate! Please select other models and try again.") | |