Instructions to use Apv/Flaubert_1406v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Apv/Flaubert_1406v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Apv/Flaubert_1406v3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Apv/Flaubert_1406v3") model = AutoModelForSequenceClassification.from_pretrained("Apv/Flaubert_1406v3") - Notebooks
- Google Colab
- Kaggle
Training in progress epoch 3
Browse files
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: 0.
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- Validation Loss: 0.5323
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- Train Accuracy: 0.7920
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- Epoch:
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## Model description
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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: 0.5829
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- Validation Loss: 0.5323
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- Train Accuracy: 0.7920
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- Epoch: 3
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## Model description
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| 0.5878 | 0.5323 | 0.7920 | 0 |
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| 0.5878 | 0.5323 | 0.7920 | 1 |
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| 0.5851 | 0.5323 | 0.7920 | 2 |
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| 0.5829 | 0.5323 | 0.7920 | 3 |
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### Framework versions
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