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marksusol
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deberta-v3-small-finetuned-ner-2048

Token Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
Inference Endpoints
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End of training

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  1. README.md +15 -12
  2. model.safetensors +1 -1
README.md CHANGED
@@ -18,10 +18,10 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0079
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- - Recall: 0.9809
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- - Precision: 0.9723
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- - Fbeta Score: 0.9805
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  ## Model description
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Recall | Precision | Fbeta Score |
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- |:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:-----------:|
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- | 0.0222 | 1.0 | 400 | 0.0201 | 0.9676 | 0.8869 | 0.9642 |
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- | 0.0144 | 2.0 | 800 | 0.0104 | 0.9826 | 0.9541 | 0.9815 |
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- | 0.0078 | 3.0 | 1200 | 0.0098 | 0.9901 | 0.9585 | 0.9889 |
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- | 0.0061 | 4.0 | 1600 | 0.0094 | 0.9844 | 0.9620 | 0.9835 |
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- | 0.0056 | 5.0 | 2000 | 0.0082 | 0.9884 | 0.9670 | 0.9876 |
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- | 0.0038 | 6.0 | 2400 | 0.0079 | 0.9809 | 0.9723 | 0.9805 |
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0036
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+ - Recall: 0.9920
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+ - Precision: 0.9866
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+ - Fbeta Score: 0.9918
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Recall | Precision | Fbeta Score |
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+ |:-------------:|:-----:|:-----:|:---------------:|:------:|:---------:|:-----------:|
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+ | 0.0085 | 1.0 | 3186 | 0.0058 | 0.9786 | 0.9693 | 0.9782 |
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+ | 0.0032 | 2.0 | 6373 | 0.0036 | 0.9869 | 0.9764 | 0.9865 |
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+ | 0.004 | 3.0 | 9559 | 0.0037 | 0.9791 | 0.9892 | 0.9795 |
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+ | 0.0014 | 4.0 | 12746 | 0.0035 | 0.9908 | 0.9817 | 0.9905 |
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+ | 0.0019 | 5.0 | 15932 | 0.0038 | 0.9903 | 0.9806 | 0.9899 |
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+ | 0.0022 | 6.0 | 19119 | 0.0032 | 0.9929 | 0.9861 | 0.9927 |
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+ | 0.0005 | 7.0 | 22305 | 0.0031 | 0.9906 | 0.9894 | 0.9905 |
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+ | 0.0003 | 8.0 | 25492 | 0.0033 | 0.9915 | 0.9855 | 0.9913 |
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+ | 0.0001 | 9.0 | 28678 | 0.0036 | 0.9920 | 0.9866 | 0.9918 |
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  ### Framework versions
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