Instructions to use alexia-allal/ner-model-camembert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alexia-allal/ner-model-camembert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="alexia-allal/ner-model-camembert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("alexia-allal/ner-model-camembert") model = AutoModelForTokenClassification.from_pretrained("alexia-allal/ner-model-camembert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 804 Bytes
14205d1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | {
"_name_or_path": "camembert-base",
"architectures": [
"CamembertForTokenClassification"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 5,
"classifier_dropout": null,
"eos_token_id": 6,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "O",
"1": "I"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"I": 1,
"O": 0
},
"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,
"position_embedding_type": "absolute",
"torch_dtype": "float32",
"transformers_version": "4.47.1",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 32005
}
|