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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_02-16-35_b3e70d60c6bb/events.out.tfevents.1720405004.b3e70d60c6bb.207.0 +3 -0
  4. runs/Jul08_02-16-35_b3e70d60c6bb/events.out.tfevents.1720405146.b3e70d60c6bb.207.1 +3 -0
  5. tokenizer.json +2 -2
  6. 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: 1.6324
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- - Precision: 0.0
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- - Recall: 0.0
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- - F1: 0.0
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- - Accuracy: 0.7314
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  ## Model description
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@@ -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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- | 0.5604 | 1.0 | 12 | 0.2449 | 0.0 | 0.0 | 0.0 | 0.9593 |
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- | 0.141 | 2.0 | 24 | 0.2360 | 0.0 | 0.0 | 0.0 | 0.9593 |
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- | 0.1111 | 3.0 | 36 | 0.2263 | 0.0 | 0.0 | 0.0 | 0.9593 |
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- | 0.0939 | 4.0 | 48 | 0.1960 | 0.0 | 0.0 | 0.0 | 0.9593 |
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- | 0.1069 | 5.0 | 60 | 0.1867 | 0.0 | 0.0 | 0.0 | 0.9593 |
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- | 0.0818 | 6.0 | 72 | 0.1879 | 0.0 | 0.0 | 0.0 | 0.9593 |
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- | 0.0614 | 7.0 | 84 | 0.1510 | 0.0 | 0.0 | 0.0 | 0.9593 |
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- | 0.0622 | 8.0 | 96 | 0.1348 | 0.0 | 0.0 | 0.0 | 0.9593 |
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- | 0.0633 | 9.0 | 108 | 0.1674 | 0.0 | 0.0 | 0.0 | 0.9593 |
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- | 0.0427 | 10.0 | 120 | 0.1632 | 0.0 | 0.0 | 0.0 | 0.9593 |
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- | 0.0389 | 11.0 | 132 | 0.1546 | 0.0 | 0.0 | 0.0 | 0.9590 |
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- | 0.0344 | 12.0 | 144 | 0.1754 | 0.125 | 0.0312 | 0.05 | 0.9618 |
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- | 0.0342 | 13.0 | 156 | 0.1702 | 0.0 | 0.0 | 0.0 | 0.9611 |
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- | 0.0272 | 14.0 | 168 | 0.1739 | 0.0526 | 0.0312 | 0.0392 | 0.9623 |
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- | 0.0224 | 15.0 | 180 | 0.1759 | 0.0526 | 0.0312 | 0.0392 | 0.9630 |
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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: 0.1345
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+ - Precision: 0.1786
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+ - Recall: 0.1351
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+ - F1: 0.1538
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+ - Accuracy: 0.9563
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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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+ | 0.8175 | 1.0 | 12 | 0.3119 | 0.0 | 0.0 | 0.0 | 0.9593 |
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+ | 0.2019 | 2.0 | 24 | 0.2414 | 0.0 | 0.0 | 0.0 | 0.9593 |
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+ | 0.1156 | 3.0 | 36 | 0.2105 | 0.0 | 0.0 | 0.0 | 0.9593 |
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+ | 0.0913 | 4.0 | 48 | 0.1831 | 0.0 | 0.0 | 0.0 | 0.9593 |
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+ | 0.0987 | 5.0 | 60 | 0.1695 | 0.0 | 0.0 | 0.0 | 0.9593 |
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+ | 0.0697 | 6.0 | 72 | 0.1727 | 0.0 | 0.0 | 0.0 | 0.9593 |
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+ | 0.0528 | 7.0 | 84 | 0.1462 | 0.0 | 0.0 | 0.0 | 0.9593 |
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+ | 0.0538 | 8.0 | 96 | 0.1441 | 0.0 | 0.0 | 0.0 | 0.9593 |
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+ | 0.0504 | 9.0 | 108 | 0.1854 | 0.0 | 0.0 | 0.0 | 0.9605 |
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+ | 0.0359 | 10.0 | 120 | 0.1516 | 0.0476 | 0.0312 | 0.0377 | 0.9641 |
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+ | 0.031 | 11.0 | 132 | 0.1836 | 0.0 | 0.0 | 0.0 | 0.9621 |
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+ | 0.038 | 12.0 | 144 | 0.1581 | 0.1579 | 0.0938 | 0.1176 | 0.9627 |
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+ | 0.0349 | 13.0 | 156 | 0.1901 | 0.0 | 0.0 | 0.0 | 0.9625 |
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+ | 0.0226 | 14.0 | 168 | 0.1740 | 0.0667 | 0.0312 | 0.0426 | 0.9648 |
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+ | 0.0198 | 15.0 | 180 | 0.1729 | 0.125 | 0.0625 | 0.0833 | 0.9639 |
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  ### Framework versions
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