| | --- |
| | license: mit |
| | --- |
| | |
| | # SpanMarker Base Model Detection |
| |
|
| | It is relative simply to determine base model of a fine-tuned SpanMarker model: |
| |
|
| | ```python |
| | import os |
| | |
| | from huggingface_hub import login, HfApi |
| | |
| | hf_token = os.environ.get("HF_TOKEN") |
| | login(token=hf_token, add_to_git_credential=True) |
| | api = HfApi() |
| | ``` |
| |
|
| | Please make sure that `HF_TOKEN` is set as environment variable. |
| |
|
| | After that, list of all SpanMarker models can be retrieved and configuration file is parsed. |
| | Please make sure that `span-marker` library is installed: |
| |
|
| | ```python |
| | from span_marker import SpanMarkerConfig |
| | |
| | f_out = open("span_marker_base_model_detection.csv", "wt") |
| | |
| | f_out.write("Nr,Model ID,Base Model ID\n") |
| | |
| | counter = 1 |
| | for span_marker_model in api.list_models(filter="span-marker"): |
| | try: |
| | config = SpanMarkerConfig.from_pretrained(span_marker_model.modelId) |
| | |
| | base_model = config.encoder["_name_or_path"] |
| | |
| | f_out.write(f"{counter},{span_marker_model.modelId},{base_model}\n") |
| | |
| | counter +=1 |
| | except Exception as e: |
| | print(e) |
| | f_out.close() |
| | ``` |