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
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Download README.md from alexia-allal/ner-model-camembert: direct link, hf CLI and curl.
- Browser
- Download file 1.69 kB
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https://huggingface.co/alexia-allal/ner-model-camembert/resolve/06533a0a631a4128fbd5e675edbb203729ee19a9/README.md
- Command line
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hf download hf://alexia-allal/ner-model-camembert@06533a0a631a4128fbd5e675edbb203729ee19a9/README.md
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curl -L -o README.md https://huggingface.co/alexia-allal/ner-model-camembert/resolve/06533a0a631a4128fbd5e675edbb203729ee19a9/README.md
1.69 kB
| library_name: transformers | |
| license: mit | |
| base_model: camembert-base | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - precision | |
| - recall | |
| - f1 | |
| - accuracy | |
| model-index: | |
| - name: ner-model-camembert | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # ner-model-camembert | |
| This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.3660 | |
| - Precision: 0.0 | |
| - Recall: 0.0 | |
| - F1: 0.0 | |
| - Accuracy: 0.8739 | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 2e-05 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - num_epochs: 2 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:| | |
| | No log | 1.0 | 24 | 0.3795 | 0.0 | 0.0 | 0.0 | 0.8739 | | |
| | No log | 2.0 | 48 | 0.3660 | 0.0 | 0.0 | 0.0 | 0.8739 | | |
| ### Framework versions | |
| - Transformers 4.47.1 | |
| - Pytorch 2.5.1+cu121 | |
| - Datasets 3.2.0 | |
| - Tokenizers 0.21.0 | |