Instructions to use BERRAMOU/camembert-math-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BERRAMOU/camembert-math-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BERRAMOU/camembert-math-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BERRAMOU/camembert-math-classification") model = AutoModelForSequenceClassification.from_pretrained("BERRAMOU/camembert-math-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files- README.md +11 -11
- config.json +0 -1
- model.safetensors +1 -1
README.md
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This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss:
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- F1 Macro: 0.
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- F1 Weighted: 0.
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- F1 Correct:
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- F1 Partiel: 0.
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- F1 Incorrect: 0.
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- Accuracy: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Weighted | F1 Correct | F1 Partiel | F1 Incorrect | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:----------:|:----------:|:------------:|:--------:|
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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 an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3711
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- F1 Macro: 0.9159
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- F1 Weighted: 0.9139
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- F1 Correct: 1.0
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- F1 Partiel: 0.8953
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- F1 Incorrect: 0.8525
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- Accuracy: 0.9143
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Weighted | F1 Correct | F1 Partiel | F1 Incorrect | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:----------:|:----------:|:------------:|:--------:|
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| 1.0746 | 1.0 | 53 | 0.8969 | 0.7007 | 0.7086 | 0.8227 | 0.7795 | 0.5 | 0.7381 |
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| 0.7178 | 2.0 | 106 | 0.5763 | 0.8772 | 0.8761 | 0.9767 | 0.8663 | 0.7885 | 0.8810 |
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| 0.5751 | 3.0 | 159 | 0.4265 | 0.9180 | 0.9169 | 1.0 | 0.9071 | 0.8468 | 0.9190 |
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| 0.4558 | 4.0 | 212 | 0.3711 | 0.9159 | 0.9139 | 1.0 | 0.8953 | 0.8525 | 0.9143 |
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### Framework versions
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config.json
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"num_hidden_layers": 12,
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"output_past": true,
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"pad_token_id": 1,
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"problem_type": "single_label_classification",
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"transformers_version": "5.0.0",
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"type_vocab_size": 1,
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"use_cache": false,
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"num_hidden_layers": 12,
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"output_past": true,
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"pad_token_id": 1,
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"transformers_version": "5.0.0",
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"type_vocab_size": 1,
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"use_cache": false,
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model.safetensors
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size 442521156
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