Instructions to use paruwka/cs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use paruwka/cs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="paruwka/cs")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("paruwka/cs") model = AutoModelForTokenClassification.from_pretrained("paruwka/cs") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
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This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Accuracy: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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### Framework versions
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This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0958
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- Accuracy: 0.9753
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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| 0.2693 | 1.0 | 125 | 0.1840 | 0.9513 |
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| 0.1658 | 2.0 | 250 | 0.1030 | 0.9737 |
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| 0.1037 | 3.0 | 375 | 0.0958 | 0.9753 |
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### Framework versions
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