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")# 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
End of training
Browse files
README.md
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This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Precision: 0.0
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- Recall: 0.0
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- F1: 0.0
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- Accuracy: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
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| No log | 1.0 |
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| No log | 2.0 |
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### Framework versions
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This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3660
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- Precision: 0.0
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- Recall: 0.0
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- F1: 0.0
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- Accuracy: 0.8739
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
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| No log | 1.0 | 24 | 0.3795 | 0.0 | 0.0 | 0.0 | 0.8739 |
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| No log | 2.0 | 48 | 0.3660 | 0.0 | 0.0 | 0.0 | 0.8739 |
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### Framework versions
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runs/Jan28_09-12-12_4431f1c5681e/events.out.tfevents.1738055537.4431f1c5681e.434.0
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size 6463
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