Instructions to use Apv/Flaubert2704_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Apv/Flaubert2704_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Apv/Flaubert2704_v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Apv/Flaubert2704_v1") model = AutoModelForSequenceClassification.from_pretrained("Apv/Flaubert2704_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress epoch 0
Browse files- README.md +6 -5
- tf_model.h5 +1 -1
README.md
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This model is a fine-tuned version of [flaubert/flaubert_base_cased](https://huggingface.co/flaubert/flaubert_base_cased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss:
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- Validation Loss: 0.
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- Epoch: 0
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## Model description
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### Training results
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| Train Loss | Validation Loss | Epoch |
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### Framework versions
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This model is a fine-tuned version of [flaubert/flaubert_base_cased](https://huggingface.co/flaubert/flaubert_base_cased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.9034
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- Validation Loss: 0.7880
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- Train Accuracy: 0.5956
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- Epoch: 0
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## Model description
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### Training results
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| Train Loss | Validation Loss | Train Accuracy | Epoch |
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| 0.9034 | 0.7880 | 0.5956 | 0 |
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### Framework versions
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tf_model.h5
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version https://git-lfs.github.com/spec/v1
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size 553167240
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version https://git-lfs.github.com/spec/v1
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size 553167240
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