Instructions to use ghadeermobasher/BioNLP13CG-Original-scibert_latest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ghadeermobasher/BioNLP13CG-Original-scibert_latest with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ghadeermobasher/BioNLP13CG-Original-scibert_latest")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ghadeermobasher/BioNLP13CG-Original-scibert_latest") model = AutoModelForTokenClassification.from_pretrained("ghadeermobasher/BioNLP13CG-Original-scibert_latest", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from ghadeermobasher/BioNLP13CG-Original-scibert_latest: direct link, hf CLI and curl.
- Browser
- Download file 974 Bytes
-
https://huggingface.co/ghadeermobasher/BioNLP13CG-Original-scibert_latest/resolve/main/config.json
- Command line
-
hf download hf://ghadeermobasher/BioNLP13CG-Original-scibert_latest/config.json
-
curl -L -o config.json https://huggingface.co/ghadeermobasher/BioNLP13CG-Original-scibert_latest/resolve/main/config.json
974 Bytes
| { | |
| "_name_or_path": "allenai/scibert_scivocab_uncased", | |
| "adapters": { | |
| "adapters": {}, | |
| "config_map": {}, | |
| "fusion_config_map": {}, | |
| "fusions": {} | |
| }, | |
| "architectures": [ | |
| "BertForTokenClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "O", | |
| "1": "B-Simple_chemical", | |
| "2": "I-Simple_chemical" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "B-Simple_chemical": "1", | |
| "I-Simple_chemical": "2", | |
| "O": "0" | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_length": 512, | |
| "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": "4.11.3", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 31090 | |
| } | |