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Model save

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  1. README.md +10 -10
  2. model.safetensors +1 -1
README.md CHANGED
@@ -21,11 +21,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [mschiesser/ner-bert-german](https://huggingface.co/mschiesser/ner-bert-german) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3068
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- - Precision: 0.1571
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  - Recall: 0.0256
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- - F1: 0.044
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- - Accuracy: 0.9302
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | No log | 0 | 0 | 2.1684 | 0.0102 | 0.0140 | 0.0118 | 0.0235 |
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- | No log | 1.0 | 5 | 0.4341 | 0.0 | 0.0 | 0.0 | 0.9205 |
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- | No log | 2.0 | 10 | 0.3826 | 0.1061 | 0.0163 | 0.0282 | 0.9261 |
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- | No log | 3.0 | 15 | 0.3359 | 0.1406 | 0.0209 | 0.0364 | 0.9294 |
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- | No log | 4.0 | 20 | 0.3159 | 0.1618 | 0.0256 | 0.0442 | 0.9298 |
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- | No log | 5.0 | 25 | 0.3068 | 0.1571 | 0.0256 | 0.044 | 0.9302 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [mschiesser/ner-bert-german](https://huggingface.co/mschiesser/ner-bert-german) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3244
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+ - Precision: 0.1803
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  - Recall: 0.0256
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+ - F1: 0.0448
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+ - Accuracy: 0.9275
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 0 | 0 | 2.1970 | 0.0028 | 0.0116 | 0.0045 | 0.0200 |
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+ | No log | 1.0 | 5 | 0.4556 | 0.0 | 0.0 | 0.0 | 0.9205 |
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+ | No log | 2.0 | 10 | 0.3726 | 0.0625 | 0.0047 | 0.0087 | 0.9223 |
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+ | No log | 3.0 | 15 | 0.3606 | 0.1351 | 0.0233 | 0.0397 | 0.9258 |
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+ | No log | 4.0 | 20 | 0.3337 | 0.1493 | 0.0233 | 0.0402 | 0.9267 |
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+ | No log | 5.0 | 25 | 0.3244 | 0.1803 | 0.0256 | 0.0448 | 0.9275 |
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  ### Framework versions
model.safetensors CHANGED
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