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mpalaval
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assignment2_attempt12

Token Classification
Transformers
PyTorch
bert
Generated from Trainer
Model card Files
xet
Community
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End of training

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  1. README.md +11 -11
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4482
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- - Precision: 0.2534
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- - Recall: 0.3814
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- - F1: 0.3045
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- - Accuracy: 0.9202
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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 | 1.0 | 347 | 0.2883 | 0.125 | 0.0859 | 0.1018 | 0.9235 |
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- | 0.2263 | 2.0 | 694 | 0.2942 | 0.1863 | 0.1684 | 0.1769 | 0.9289 |
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- | 0.1098 | 3.0 | 1041 | 0.3449 | 0.2450 | 0.2543 | 0.2496 | 0.9312 |
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- | 0.1098 | 4.0 | 1388 | 0.3747 | 0.2334 | 0.3265 | 0.2722 | 0.9237 |
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- | 0.0486 | 5.0 | 1735 | 0.4383 | 0.2656 | 0.2784 | 0.2718 | 0.9277 |
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- | 0.0257 | 6.0 | 2082 | 0.4482 | 0.2534 | 0.3814 | 0.3045 | 0.9202 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.4465
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+ - Precision: 0.2230
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+ - Recall: 0.2268
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+ - F1: 0.2249
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+ - Accuracy: 0.9262
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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 | 1.0 | 347 | 0.2699 | 0.1554 | 0.1581 | 0.1567 | 0.9238 |
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+ | 0.3071 | 2.0 | 694 | 0.3111 | 0.1843 | 0.1375 | 0.1575 | 0.9302 |
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+ | 0.1235 | 3.0 | 1041 | 0.3048 | 0.2164 | 0.2543 | 0.2338 | 0.9280 |
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+ | 0.1235 | 4.0 | 1388 | 0.3606 | 0.1920 | 0.2302 | 0.2094 | 0.9208 |
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+ | 0.0592 | 5.0 | 1735 | 0.4584 | 0.2112 | 0.1684 | 0.1874 | 0.9280 |
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+ | 0.0304 | 6.0 | 2082 | 0.4465 | 0.2230 | 0.2268 | 0.2249 | 0.9262 |
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  ### Framework versions
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