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xlm-roberta-base-finetuned-ner-thesis-dseb

Token Classification
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xlm-roberta
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  1. README.md +20 -20
  2. model.safetensors +1 -1
  3. runs/Jul08_10-02-39_710793e53bac/events.out.tfevents.1720432961.710793e53bac.504.7 +3 -0
  4. runs/Jul08_10-02-39_710793e53bac/events.out.tfevents.1720433196.710793e53bac.504.8 +3 -0
  5. training_args.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 [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1100
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- - Precision: 0.4783
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  - Recall: 0.7857
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- - F1: 0.5946
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- - Accuracy: 0.9853
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  ## Model description
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@@ -43,7 +43,7 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 1e-05
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
@@ -55,21 +55,21 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 0.056 | 1.0 | 50 | 0.1647 | 0.2593 | 0.175 | 0.2090 | 0.9608 |
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- | 0.0154 | 2.0 | 100 | 0.2252 | 0.3846 | 0.25 | 0.3030 | 0.9629 |
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- | 0.0078 | 3.0 | 150 | 0.2033 | 0.5366 | 0.55 | 0.5432 | 0.9669 |
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- | 0.0032 | 4.0 | 200 | 0.2098 | 0.5385 | 0.525 | 0.5316 | 0.9673 |
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- | 0.0022 | 5.0 | 250 | 0.2502 | 0.4167 | 0.375 | 0.3947 | 0.9651 |
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- | 0.0025 | 6.0 | 300 | 0.2565 | 0.4516 | 0.35 | 0.3944 | 0.9655 |
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- | 0.0039 | 7.0 | 350 | 0.2308 | 0.3684 | 0.35 | 0.3590 | 0.9667 |
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- | 0.0029 | 8.0 | 400 | 0.2048 | 0.4091 | 0.45 | 0.4286 | 0.9723 |
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- | 0.004 | 9.0 | 450 | 0.1996 | 0.6047 | 0.65 | 0.6265 | 0.9725 |
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- | 0.0012 | 10.0 | 500 | 0.3041 | 0.4483 | 0.325 | 0.3768 | 0.9635 |
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- | 0.0014 | 11.0 | 550 | 0.2086 | 0.6222 | 0.7 | 0.6588 | 0.9709 |
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- | 0.0003 | 12.0 | 600 | 0.2162 | 0.5682 | 0.625 | 0.5952 | 0.9705 |
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- | 0.0003 | 13.0 | 650 | 0.2180 | 0.5952 | 0.625 | 0.6098 | 0.9705 |
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- | 0.0003 | 14.0 | 700 | 0.2186 | 0.5814 | 0.625 | 0.6024 | 0.9707 |
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- | 0.0012 | 15.0 | 750 | 0.2196 | 0.5814 | 0.625 | 0.6024 | 0.9709 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1007
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+ - Precision: 0.5789
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  - Recall: 0.7857
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+ - F1: 0.6667
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+ - Accuracy: 0.9871
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
 
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.0117 | 1.0 | 50 | 0.2576 | 0.3256 | 0.35 | 0.3373 | 0.9523 |
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+ | 0.0454 | 2.0 | 100 | 0.2768 | 0.5714 | 0.3 | 0.3934 | 0.9590 |
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+ | 0.0188 | 3.0 | 150 | 0.1758 | 0.6429 | 0.45 | 0.5294 | 0.9746 |
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+ | 0.0144 | 4.0 | 200 | 0.3266 | 0.5714 | 0.2 | 0.2963 | 0.9601 |
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+ | 0.0134 | 5.0 | 250 | 0.2405 | 0.7143 | 0.375 | 0.4918 | 0.9667 |
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+ | 0.0038 | 6.0 | 300 | 0.1727 | 0.5660 | 0.75 | 0.6452 | 0.9759 |
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+ | 0.0036 | 7.0 | 350 | 0.1335 | 0.7561 | 0.775 | 0.7654 | 0.9835 |
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+ | 0.0047 | 8.0 | 400 | 0.1240 | 0.7111 | 0.8 | 0.7529 | 0.9836 |
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+ | 0.0013 | 9.0 | 450 | 0.1468 | 0.8 | 0.7 | 0.7467 | 0.9782 |
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+ | 0.0001 | 10.0 | 500 | 0.1222 | 0.7368 | 0.7 | 0.7179 | 0.9811 |
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+ | 0.0 | 11.0 | 550 | 0.1261 | 0.7368 | 0.7 | 0.7179 | 0.9817 |
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+ | 0.0 | 12.0 | 600 | 0.1273 | 0.7368 | 0.7 | 0.7179 | 0.9817 |
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+ | 0.0 | 13.0 | 650 | 0.1293 | 0.7368 | 0.7 | 0.7179 | 0.9809 |
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+ | 0.0001 | 14.0 | 700 | 0.1367 | 0.7838 | 0.725 | 0.7532 | 0.9809 |
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+ | 0.0003 | 15.0 | 750 | 0.1383 | 0.8056 | 0.725 | 0.7632 | 0.9808 |
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  ### Framework versions
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