Token Classification
Transformers
Safetensors
Hebrew
English
bert
named-entity-recognition
hebrew-manuscripts
marc
role-classification
Instructions to use alexgoldberg/hebrew-manuscript-joint-ner-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alexgoldberg/hebrew-manuscript-joint-ner-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="alexgoldberg/hebrew-manuscript-joint-ner-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("alexgoldberg/hebrew-manuscript-joint-ner-v2") model = AutoModelForTokenClassification.from_pretrained("alexgoldberg/hebrew-manuscript-joint-ner-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "add_cross_attention": false, | |
| "architectures": [ | |
| "BertForTokenClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.2, | |
| "bos_token_id": null, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "eos_token_id": null, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.2, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "O", | |
| "1": "B-AUTHOR", | |
| "2": "I-AUTHOR", | |
| "3": "B-TRANSCRIBER", | |
| "4": "I-TRANSCRIBER", | |
| "5": "B-OWNER", | |
| "6": "I-OWNER", | |
| "7": "B-CENSOR", | |
| "8": "I-CENSOR", | |
| "9": "B-TRANSLATOR", | |
| "10": "I-TRANSLATOR", | |
| "11": "B-COMMENTATOR", | |
| "12": "I-COMMENTATOR" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "is_decoder": false, | |
| "label2id": { | |
| "B-AUTHOR": 1, | |
| "B-CENSOR": 7, | |
| "B-COMMENTATOR": 11, | |
| "B-OWNER": 5, | |
| "B-TRANSCRIBER": 3, | |
| "B-TRANSLATOR": 9, | |
| "I-AUTHOR": 2, | |
| "I-CENSOR": 8, | |
| "I-COMMENTATOR": 12, | |
| "I-OWNER": 6, | |
| "I-TRANSCRIBER": 4, | |
| "I-TRANSLATOR": 10, | |
| "O": 0 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "newmodern": true, | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.3.0", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 128000 | |
| } | |