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Flair Base Model Detector
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| import flair | |
| import os | |
| import pickle | |
| import shutil | |
| from huggingface_hub import login, HfApi | |
| from flair.models import SequenceTagger | |
| from flair.embeddings import StackedEmbeddings, TransformerWordEmbeddings | |
| from pathlib import Path | |
| # Please adjust! | |
| flair.cache_root = Path("/mnt/datasets/.flair") | |
| errors = [] | |
| def determine_base_model(flair_model_name: str) -> str: | |
| try: | |
| tagger = SequenceTagger.load(flair_model_name) | |
| if isinstance(tagger.embeddings, StackedEmbeddings): | |
| for embedding in tagger.embeddings.embeddings: | |
| if isinstance(embedding, TransformerWordEmbeddings): | |
| return embedding.model.name_or_path | |
| elif isinstance(tagger.embeddings, TransformerWordEmbeddings): | |
| return tagger.embeddings.model.name_or_path | |
| except Exception as e: | |
| error_message = f"Could not parse Flair Model {flair_model_name} :" + str(e) | |
| errors.append(error_message) | |
| print(error_message) | |
| return "" | |
| hf_token = os.environ.get("HF_TOKEN") | |
| login(token=hf_token, add_to_git_credential=True) | |
| api = HfApi() | |
| base_model_mapping = {} | |
| for flair_model in api.list_models(filter="flair"): | |
| print("Detecting base model for:", flair_model.modelId) | |
| base_model = determine_base_model(flair_model.modelId) | |
| if not base_model: | |
| continue | |
| print("Detected Base model for", flair_model.modelId, "is:", base_model) | |
| base_model_mapping[flair_model.modelId] = base_model | |
| with open("base_model_mapping.pkl", "wb") as f_out: | |
| pickle.dump(base_model_mapping, f_out, pickle.HIGHEST_PROTOCOL) | |
| with open("errors.pkl", "wb") as f_out: | |
| pickle.dump(errors, f_out, pickle.HIGHEST_PROTOCOL) |
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🤗 Flair Base Model Detector
In order to get the script working, the following things needs to be configured:
HF_TOKENenvironment variable: just visit Access Tokens site and copy your Access Token. Then set it viaexport HF_TOKEN="<your-token>"on commandline (add leading space so it won't occur in your shell history).flair.cache_root: set it to e.g. a NAS storage05.09.2023, 22:31: Stats
The script was initially executed. Runtime was ~1.5 hours and it downloaded ~85GB of data.
Base Model Table can be created with the following code:
It then outputs: