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| from typing import Dict, Union | |
| import requests | |
| from huggingface_hub import HfApi, ModelFilter, dataset_info | |
| AUTOTRAIN_TASK_TO_HUB_TASK = { | |
| "binary_classification": "text-classification", | |
| "multi_class_classification": "text-classification", | |
| # "multi_label_classification": "text-classification", # Not fully supported in AutoTrain | |
| "entity_extraction": "token-classification", | |
| "extractive_question_answering": "question-answering", | |
| "translation": "translation", | |
| "summarization": "summarization", | |
| # "single_column_regression": 10, | |
| } | |
| HUB_TASK_TO_AUTOTRAIN_TASK = {v: k for k, v in AUTOTRAIN_TASK_TO_HUB_TASK.items()} | |
| api = HfApi() | |
| def get_auth_headers(token: str, prefix: str = "autonlp"): | |
| return {"Authorization": f"{prefix} {token}"} | |
| def http_post(path: str, token: str, payload=None, domain: str = None, params=None) -> requests.Response: | |
| """HTTP POST request to the AutoNLP API, raises UnreachableAPIError if the API cannot be reached""" | |
| try: | |
| response = requests.post( | |
| url=domain + path, | |
| json=payload, | |
| headers=get_auth_headers(token=token), | |
| allow_redirects=True, | |
| params=params, | |
| ) | |
| except requests.exceptions.ConnectionError: | |
| print("❌ Failed to reach AutoNLP API, check your internet connection") | |
| response.raise_for_status() | |
| return response | |
| def http_get(path: str, domain: str, token: str = None, params: dict = None) -> requests.Response: | |
| """HTTP POST request to `path`, raises UnreachableAPIError if the API cannot be reached""" | |
| try: | |
| response = requests.get( | |
| url=domain + path, | |
| headers=get_auth_headers(token=token), | |
| allow_redirects=True, | |
| params=params, | |
| ) | |
| except requests.exceptions.ConnectionError: | |
| print(f"❌ Failed to reach {path}, check your internet connection") | |
| response.raise_for_status() | |
| return response | |
| def get_metadata(dataset_name: str) -> Union[Dict, None]: | |
| data = dataset_info(dataset_name) | |
| if data.cardData is not None and "train-eval-index" in data.cardData.keys(): | |
| return data.cardData["train-eval-index"] | |
| else: | |
| return None | |
| def get_compatible_models(task, dataset_name): | |
| # TODO: relax filter on PyTorch models once supported in AutoTrain | |
| filt = ModelFilter( | |
| task=AUTOTRAIN_TASK_TO_HUB_TASK[task], | |
| trained_dataset=dataset_name, | |
| library=["transformers", "pytorch"], | |
| ) | |
| compatible_models = api.list_models(filter=filt) | |
| return sorted([model.modelId for model in compatible_models]) | |
| def get_key(col_mapping, val): | |
| for key, value in col_mapping.items(): | |
| if val == value: | |
| return key | |
| return "key doesn't exist" | |
| def format_col_mapping(col_mapping: dict) -> dict: | |
| for k, v in col_mapping["answers"].items(): | |
| col_mapping[f"answers.{k}"] = f"answers.{v}" | |
| del col_mapping["answers"] | |
| return col_mapping | |