Token Classification
Transformers
Safetensors
French
bert
newsagency
ner
historical
impresso
multilingual
Instructions to use impresso-project/ner-newsagency-bert-fr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use impresso-project/ner-newsagency-bert-fr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="impresso-project/ner-newsagency-bert-fr")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("impresso-project/ner-newsagency-bert-fr") model = AutoModelForTokenClassification.from_pretrained("impresso-project/ner-newsagency-bert-fr") - Notebooks
- Google Colab
- Kaggle
Upload BertForTokenClassification
Browse files- config.json +1 -10
- model.safetensors +2 -2
config.json
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"custom_pipelines": {
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"newsagency-ner": {
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"impl": "newsagency_ner.NewsAgencyModelPipeline",
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"pt": [
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"AutoModelForTokenClassification"
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],
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"tf": []
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}
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},
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"position_embedding_type": "absolute",
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"problem_type": "single_label_classification",
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"torch_dtype": "float32",
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"transformers_version": "4.
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 32000
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"position_embedding_type": "absolute",
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"problem_type": "single_label_classification",
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"torch_dtype": "float32",
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"transformers_version": "4.45.0.dev0",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 32000
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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version https://git-lfs.github.com/spec/v1
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size 440136504
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