Instructions to use carissa1/SimplyLaw_Legal_Questions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use carissa1/SimplyLaw_Legal_Questions with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("<base-model-id>") model.load_adapter("carissa1/SimplyLaw_Legal_Questions", set_active=True) - Notebooks
- Google Colab
- Kaggle
Download reader_model/processor_config.json from carissa1/SimplyLaw_Legal_Questions: direct link, hf CLI and curl.
- Browser
- Download file 685 Bytes
-
https://huggingface.co/carissa1/SimplyLaw_Legal_Questions/resolve/main/reader_model/processor_config.json
- Command line
-
hf download hf://carissa1/SimplyLaw_Legal_Questions/reader_model/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/carissa1/SimplyLaw_Legal_Questions/resolve/main/reader_model/processor_config.json
685 Bytes
| {"data_dir": "data", "dev_filename": "dev-v2.0.json", "dev_split": 0, "doc_stride": 128, "max_answers": null, "max_query_length": 64, "max_seq_len": 256, "multithreading_rust": true, "ph_output_type": "per_token_squad", "proxies": null, "sp_toks_end": 1, "sp_toks_mid": 2, "sp_toks_start": 1, "tasks": {"question_answering": {"label_list": ["start_token", "end_token"], "metric": "squad", "label_tensor_name": "question_answering_label_ids", "label_name": "question_answering_label", "label_column_name": null, "text_column_name": null, "task_type": null}}, "test_filename": null, "train_filename": "train-v2.0.json", "tokenizer": "RobertaTokenizerFast", "processor": "SquadProcessor"} |