Token Classification
Transformers
Safetensors
English
albert
ner
named-entity-recognition
indic-languages
bert
medical-nlp
regulatory
pharmaceutical
Instructions to use sharkdodo/Indic-Bert-NER-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sharkdodo/Indic-Bert-NER-Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="sharkdodo/Indic-Bert-NER-Model")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("sharkdodo/Indic-Bert-NER-Model") model = AutoModelForTokenClassification.from_pretrained("sharkdodo/Indic-Bert-NER-Model") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "AlbertForTokenClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0, | |
| "bos_token_id": 2, | |
| "classifier_dropout_prob": 0.1, | |
| "down_scale_factor": 1, | |
| "dtype": "float32", | |
| "embedding_size": 128, | |
| "eos_token_id": 3, | |
| "gap_size": 0, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "B-AADHAAR", | |
| "1": "B-ABHA", | |
| "2": "B-ADDRESS_FULL", | |
| "3": "B-ADVERSE_EVENT", | |
| "4": "B-AGE", | |
| "5": "B-CTRI_NUMBER", | |
| "6": "B-DATE_DOB", | |
| "7": "B-DATE_GENERIC", | |
| "8": "B-DIAGNOSIS", | |
| "9": "B-DRUG_DOSE", | |
| "10": "B-DRUG_NAME", | |
| "11": "B-DRUG_ROUTE", | |
| "12": "B-EC_REG_NUMBER", | |
| "13": "B-EMAIL", | |
| "14": "B-LICENSE_NUMBER", | |
| "15": "B-LOCATION_CITY", | |
| "16": "B-LOCATION_PINCODE", | |
| "17": "B-LOCATION_STATE", | |
| "18": "B-MRN", | |
| "19": "B-ORG_CRO", | |
| "20": "B-ORG_EC", | |
| "21": "B-ORG_HOSPITAL", | |
| "22": "B-ORG_SPONSOR", | |
| "23": "B-OUTCOME", | |
| "24": "B-PAN", | |
| "25": "B-PERSON_GENERIC", | |
| "26": "B-PERSON_INVESTIGATOR", | |
| "27": "B-PERSON_PATIENT", | |
| "28": "B-PHONE_IN", | |
| "29": "B-PROTOCOL_NUMBER", | |
| "30": "B-SEVERITY", | |
| "31": "B-SITE_CODE", | |
| "32": "B-VITAL_SIGN", | |
| "33": "I-AADHAAR", | |
| "34": "I-ABHA", | |
| "35": "I-ADDRESS_FULL", | |
| "36": "I-ADVERSE_EVENT", | |
| "37": "I-AGE", | |
| "38": "I-DIAGNOSIS", | |
| "39": "I-DRUG_DOSE", | |
| "40": "I-DRUG_NAME", | |
| "41": "I-EC_REG_NUMBER", | |
| "42": "I-LOCATION_STATE", | |
| "43": "I-ORG_CRO", | |
| "44": "I-ORG_EC", | |
| "45": "I-ORG_HOSPITAL", | |
| "46": "I-ORG_SPONSOR", | |
| "47": "I-OUTCOME", | |
| "48": "I-PAN", | |
| "49": "I-PERSON_GENERIC", | |
| "50": "I-PERSON_INVESTIGATOR", | |
| "51": "I-PERSON_PATIENT", | |
| "52": "I-PHONE_IN", | |
| "53": "I-PROTOCOL_NUMBER", | |
| "54": "I-SEVERITY", | |
| "55": "I-VITAL_SIGN", | |
| "56": "O" | |
| }, | |
| "initializer_range": 0.02, | |
| "inner_group_num": 1, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "B-AADHAAR": 0, | |
| "B-ABHA": 1, | |
| "B-ADDRESS_FULL": 2, | |
| "B-ADVERSE_EVENT": 3, | |
| "B-AGE": 4, | |
| "B-CTRI_NUMBER": 5, | |
| "B-DATE_DOB": 6, | |
| "B-DATE_GENERIC": 7, | |
| "B-DIAGNOSIS": 8, | |
| "B-DRUG_DOSE": 9, | |
| "B-DRUG_NAME": 10, | |
| "B-DRUG_ROUTE": 11, | |
| "B-EC_REG_NUMBER": 12, | |
| "B-EMAIL": 13, | |
| "B-LICENSE_NUMBER": 14, | |
| "B-LOCATION_CITY": 15, | |
| "B-LOCATION_PINCODE": 16, | |
| "B-LOCATION_STATE": 17, | |
| "B-MRN": 18, | |
| "B-ORG_CRO": 19, | |
| "B-ORG_EC": 20, | |
| "B-ORG_HOSPITAL": 21, | |
| "B-ORG_SPONSOR": 22, | |
| "B-OUTCOME": 23, | |
| "B-PAN": 24, | |
| "B-PERSON_GENERIC": 25, | |
| "B-PERSON_INVESTIGATOR": 26, | |
| "B-PERSON_PATIENT": 27, | |
| "B-PHONE_IN": 28, | |
| "B-PROTOCOL_NUMBER": 29, | |
| "B-SEVERITY": 30, | |
| "B-SITE_CODE": 31, | |
| "B-VITAL_SIGN": 32, | |
| "I-AADHAAR": 33, | |
| "I-ABHA": 34, | |
| "I-ADDRESS_FULL": 35, | |
| "I-ADVERSE_EVENT": 36, | |
| "I-AGE": 37, | |
| "I-DIAGNOSIS": 38, | |
| "I-DRUG_DOSE": 39, | |
| "I-DRUG_NAME": 40, | |
| "I-EC_REG_NUMBER": 41, | |
| "I-LOCATION_STATE": 42, | |
| "I-ORG_CRO": 43, | |
| "I-ORG_EC": 44, | |
| "I-ORG_HOSPITAL": 45, | |
| "I-ORG_SPONSOR": 46, | |
| "I-OUTCOME": 47, | |
| "I-PAN": 48, | |
| "I-PERSON_GENERIC": 49, | |
| "I-PERSON_INVESTIGATOR": 50, | |
| "I-PERSON_PATIENT": 51, | |
| "I-PHONE_IN": 52, | |
| "I-PROTOCOL_NUMBER": 53, | |
| "I-SEVERITY": 54, | |
| "I-VITAL_SIGN": 55, | |
| "O": 56 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "albert", | |
| "net_structure_type": 0, | |
| "num_attention_heads": 12, | |
| "num_hidden_groups": 1, | |
| "num_hidden_layers": 12, | |
| "num_memory_blocks": 0, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "transformers_version": "4.57.6", | |
| "type_vocab_size": 2, | |
| "vocab_size": 200000 | |
| } | |