Token Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use nickprock/bert-finetuned-ner-ontonotes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nickprock/bert-finetuned-ner-ontonotes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="nickprock/bert-finetuned-ner-ontonotes")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("nickprock/bert-finetuned-ner-ontonotes") model = AutoModelForTokenClassification.from_pretrained("nickprock/bert-finetuned-ner-ontonotes") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "bert-base-cased", | |
| "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-CARDINAL", | |
| "10": "I-LAW", | |
| "11": "B-ORG", | |
| "12": "I-ORG", | |
| "13": "B-PERCENT", | |
| "14": "I-PERCENT", | |
| "15": "B-ORDINAL", | |
| "16": "B-MONEY", | |
| "17": "I-MONEY", | |
| "18": "B-WORK_OF_ART", | |
| "19": "I-WORK_OF_ART", | |
| "2": "B-DATE", | |
| "20": "B-FAC", | |
| "21": "B-TIME", | |
| "22": "I-CARDINAL", | |
| "23": "B-LOC", | |
| "24": "B-QUANTITY", | |
| "25": "I-QUANTITY", | |
| "26": "I-NORP", | |
| "27": "I-LOC", | |
| "28": "B-PRODUCT", | |
| "29": "I-TIME", | |
| "3": "I-DATE", | |
| "30": "B-EVENT", | |
| "31": "I-EVENT", | |
| "32": "I-FAC", | |
| "33": "B-LANGUAGE", | |
| "34": "I-PRODUCT", | |
| "35": "I-ORDINAL", | |
| "36": "I-LANGUAGE", | |
| "4": "B-PERSON", | |
| "5": "I-PERSON", | |
| "6": "B-NORP", | |
| "7": "B-GPE", | |
| "8": "I-GPE", | |
| "9": "B-LAW" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "B-CARDINAL": "1", | |
| "B-DATE": "2", | |
| "B-EVENT": "30", | |
| "B-FAC": "20", | |
| "B-GPE": "7", | |
| "B-LANGUAGE": "33", | |
| "B-LAW": "9", | |
| "B-LOC": "23", | |
| "B-MONEY": "16", | |
| "B-NORP": "6", | |
| "B-ORDINAL": "15", | |
| "B-ORG": "11", | |
| "B-PERCENT": "13", | |
| "B-PERSON": "4", | |
| "B-PRODUCT": "28", | |
| "B-QUANTITY": "24", | |
| "B-TIME": "21", | |
| "B-WORK_OF_ART": "18", | |
| "I-CARDINAL": "22", | |
| "I-DATE": "3", | |
| "I-EVENT": "31", | |
| "I-FAC": "32", | |
| "I-GPE": "8", | |
| "I-LANGUAGE": "36", | |
| "I-LAW": "10", | |
| "I-LOC": "27", | |
| "I-MONEY": "17", | |
| "I-NORP": "26", | |
| "I-ORDINAL": "35", | |
| "I-ORG": "12", | |
| "I-PERCENT": "14", | |
| "I-PERSON": "5", | |
| "I-PRODUCT": "34", | |
| "I-QUANTITY": "25", | |
| "I-TIME": "29", | |
| "I-WORK_OF_ART": "19", | |
| "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": "4.22.1", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 28996 | |
| } | |