Token Classification
Transformers
PyTorch
TensorBoard
electra
Generated from Trainer
Eval Results (legacy)
Instructions to use chintagunta85/electramed-small-deid2014-ner-v5-classweights with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chintagunta85/electramed-small-deid2014-ner-v5-classweights with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="chintagunta85/electramed-small-deid2014-ner-v5-classweights")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("chintagunta85/electramed-small-deid2014-ner-v5-classweights") model = AutoModelForTokenClassification.from_pretrained("chintagunta85/electramed-small-deid2014-ner-v5-classweights") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "giacomomiolo/electramed_small_scivocab", | |
| "architectures": [ | |
| "ElectraForTokenClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "embedding_size": 128, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 256, | |
| "id2label": { | |
| "0": "B-ZIP", | |
| "1": "B-FAX", | |
| "10": "B-CITY", | |
| "11": "I-DEVICE", | |
| "12": "I-MEDICALRECORD", | |
| "13": "I-STREET", | |
| "14": "B-MEDICALRECORD", | |
| "15": "B-EMAIL", | |
| "16": "I-AGE", | |
| "17": "B-HOSPITAL", | |
| "18": "B-ORGANIZATION", | |
| "19": "I-ZIP", | |
| "2": "B-DEVICE", | |
| "20": "B-COUNTRY", | |
| "21": "B-BIOID", | |
| "22": "B-URL", | |
| "23": "B-DATE", | |
| "24": "I-ORGANIZATION", | |
| "25": "B-STATE", | |
| "26": "B-PATIENT", | |
| "27": "I-STATE", | |
| "28": "I-LOCATION_OTHER", | |
| "29": "B-STREET", | |
| "3": "I-CITY", | |
| "30": "B-USERNAME", | |
| "31": "B-PROFESSION", | |
| "32": "I-IDNUM", | |
| "33": "I-HOSPITAL", | |
| "34": "B-IDNUM", | |
| "35": "B-DOCTOR", | |
| "36": "B-PHONE", | |
| "37": "I-URL", | |
| "38": "B-HEALTHPLAN", | |
| "39": "B-AGE", | |
| "4": "I-FAX", | |
| "40": "I-DOCTOR", | |
| "41": "O", | |
| "42": "I-PROFESSION", | |
| "43": "I-PATIENT", | |
| "5": "I-DATE", | |
| "6": "B-LOCATION_OTHER", | |
| "7": "I-PHONE", | |
| "8": "I-HEALTHPLAN", | |
| "9": "I-COUNTRY" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1024, | |
| "label2id": { | |
| "B-AGE": "39", | |
| "B-BIOID": "21", | |
| "B-CITY": "10", | |
| "B-COUNTRY": "20", | |
| "B-DATE": "23", | |
| "B-DEVICE": "2", | |
| "B-DOCTOR": "35", | |
| "B-EMAIL": "15", | |
| "B-FAX": "1", | |
| "B-HEALTHPLAN": "38", | |
| "B-HOSPITAL": "17", | |
| "B-IDNUM": "34", | |
| "B-LOCATION_OTHER": "6", | |
| "B-MEDICALRECORD": "14", | |
| "B-ORGANIZATION": "18", | |
| "B-PATIENT": "26", | |
| "B-PHONE": "36", | |
| "B-PROFESSION": "31", | |
| "B-STATE": "25", | |
| "B-STREET": "29", | |
| "B-URL": "22", | |
| "B-USERNAME": "30", | |
| "B-ZIP": "0", | |
| "I-AGE": "16", | |
| "I-CITY": "3", | |
| "I-COUNTRY": "9", | |
| "I-DATE": "5", | |
| "I-DEVICE": "11", | |
| "I-DOCTOR": "40", | |
| "I-FAX": "4", | |
| "I-HEALTHPLAN": "8", | |
| "I-HOSPITAL": "33", | |
| "I-IDNUM": "32", | |
| "I-LOCATION_OTHER": "28", | |
| "I-MEDICALRECORD": "12", | |
| "I-ORGANIZATION": "24", | |
| "I-PATIENT": "43", | |
| "I-PHONE": "7", | |
| "I-PROFESSION": "42", | |
| "I-STATE": "27", | |
| "I-STREET": "13", | |
| "I-URL": "37", | |
| "I-ZIP": "19", | |
| "O": "41" | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "electra", | |
| "num_attention_heads": 4, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "summary_activation": "gelu", | |
| "summary_last_dropout": 0.1, | |
| "summary_type": "first", | |
| "summary_use_proj": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.22.1", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 30522 | |
| } | |