Instructions to use praneethvasarla/bio-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use praneethvasarla/bio-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="praneethvasarla/bio-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("praneethvasarla/bio-bert") model = AutoModelForTokenClassification.from_pretrained("praneethvasarla/bio-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from praneethvasarla/bio-bert: direct link, hf CLI and curl.
- Browser
- Download file 1.8 kB
-
https://huggingface.co/praneethvasarla/bio-bert/resolve/main/config.json
- Command line
-
hf download hf://praneethvasarla/bio-bert/config.json
-
curl -L -o config.json https://huggingface.co/praneethvasarla/bio-bert/resolve/main/config.json
1.8 kB
| { | |
| "_name_or_path": "bert-base-uncased", | |
| "architectures": [ | |
| "BertForTokenClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "O", | |
| "1": "B-CHEMICAL", | |
| "2": "I-CHEMICAL", | |
| "3": "B-EUKARYOTE", | |
| "4": "I-EUKARYOTE", | |
| "5": "B-ORGANISM", | |
| "6": "I-ORGANISM", | |
| "7": "B-THERAPEUTIC_OR_PREVENTIVE_PROCEDURE", | |
| "8": "I-THERAPEUTIC_OR_PREVENTIVE_PROCEDURE", | |
| "9": "B-VIRUS", | |
| "10": "I-VIRUS", | |
| "11": "B-TISSUE", | |
| "12": "I-TISSUE", | |
| "13": "B-CORONAVIRUS", | |
| "14": "I-CORONAVIRUS", | |
| "15": "B-GENE_OR_GENOME", | |
| "16": "I-GENE_OR_GENOME", | |
| "17": "B-DISEASE_OR_SYNDROME", | |
| "18": "I-DISEASE_OR_SYNDROME", | |
| "19": "B-CELL", | |
| "20": "I-CELL" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "B-CELL": 19, | |
| "B-CHEMICAL": 1, | |
| "B-CORONAVIRUS": 13, | |
| "B-DISEASE_OR_SYNDROME": 17, | |
| "B-EUKARYOTE": 3, | |
| "B-GENE_OR_GENOME": 15, | |
| "B-ORGANISM": 5, | |
| "B-THERAPEUTIC_OR_PREVENTIVE_PROCEDURE": 7, | |
| "B-TISSUE": 11, | |
| "B-VIRUS": 9, | |
| "I-CELL": 20, | |
| "I-CHEMICAL": 2, | |
| "I-CORONAVIRUS": 14, | |
| "I-DISEASE_OR_SYNDROME": 18, | |
| "I-EUKARYOTE": 4, | |
| "I-GENE_OR_GENOME": 16, | |
| "I-ORGANISM": 6, | |
| "I-THERAPEUTIC_OR_PREVENTIVE_PROCEDURE": 8, | |
| "I-TISSUE": 12, | |
| "I-VIRUS": 10, | |
| "O": 0 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.35.2", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 30522 | |
| } | |