Instructions to use TypicaAI/magbert-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TypicaAI/magbert-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="TypicaAI/magbert-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("TypicaAI/magbert-ner") model = AutoModelForTokenClassification.from_pretrained("TypicaAI/magbert-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "CamembertForTokenClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 5, | |
| "eos_token_id": 6, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "I-PERSON", | |
| "1": "B-PERSON", | |
| "2": "I-MONEY", | |
| "3": "O", | |
| "4": "I-LOC", | |
| "5": "B-ORG", | |
| "6": "I-FAC", | |
| "7": "I-TIME", | |
| "8": "B-PRODUCT", | |
| "9": "I-DATE", | |
| "10": "I-PERCENT", | |
| "11": "B-PERCENT", | |
| "12": "B-EVENT", | |
| "13": "I-EVENT", | |
| "14": "I-PRODUCT", | |
| "15": "B-FAC", | |
| "16": "B-LOC", | |
| "17": "B-DATE", | |
| "18": "B-NORP", | |
| "19": "B-TIME", | |
| "20": "B-GPE", | |
| "21": "I-NORP", | |
| "22": "B-LAW", | |
| "23": "B-MONEY", | |
| "24": "I-LAW", | |
| "25": "I-ORG", | |
| "26": "I-GPE", | |
| "27": "PAD" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "B-DATE": 17, | |
| "B-EVENT": 12, | |
| "B-FAC": 15, | |
| "B-GPE": 20, | |
| "B-LAW": 22, | |
| "B-LOC": 16, | |
| "B-MONEY": 23, | |
| "B-NORP": 18, | |
| "B-ORG": 5, | |
| "B-PERCENT": 11, | |
| "B-PERSON": 1, | |
| "B-PRODUCT": 8, | |
| "B-TIME": 19, | |
| "I-DATE": 9, | |
| "I-EVENT": 13, | |
| "I-FAC": 6, | |
| "I-GPE": 26, | |
| "I-LAW": 24, | |
| "I-LOC": 4, | |
| "I-MONEY": 2, | |
| "I-NORP": 21, | |
| "I-ORG": 25, | |
| "I-PERCENT": 10, | |
| "I-PERSON": 0, | |
| "I-PRODUCT": 14, | |
| "I-TIME": 7, | |
| "O": 3, | |
| "PAD": 27 | |
| }, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 514, | |
| "model_type": "camembert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "output_past": true, | |
| "pad_token_id": 1, | |
| "type_vocab_size": 1, | |
| "vocab_size": 32005 | |
| } | |