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CNEC2_0_extended_xlm-roberta-large

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xlm-roberta
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  1. README.md +25 -26
  2. model.safetensors +1 -1
README.md CHANGED
@@ -25,16 +25,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.8675623800383877
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  - name: Recall
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  type: recall
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- value: 0.8972704714640198
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  - name: F1
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  type: f1
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- value: 0.8821663820444011
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  - name: Accuracy
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  type: accuracy
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- value: 0.9754391100702576
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -44,11 +44,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the cnec dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1456
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- - Precision: 0.8676
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- - Recall: 0.8973
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- - F1: 0.8822
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- - Accuracy: 0.9754
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  ## Model description
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@@ -73,26 +73,25 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 8
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 0.3439 | 0.56 | 500 | 0.1575 | 0.7882 | 0.8015 | 0.7948 | 0.9605 |
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- | 0.1636 | 1.12 | 1000 | 0.1242 | 0.8071 | 0.8432 | 0.8248 | 0.9699 |
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- | 0.1347 | 1.68 | 1500 | 0.1246 | 0.8273 | 0.8486 | 0.8378 | 0.9688 |
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- | 0.105 | 2.24 | 2000 | 0.1276 | 0.8428 | 0.8645 | 0.8535 | 0.9727 |
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- | 0.0942 | 2.8 | 2500 | 0.1263 | 0.8412 | 0.8809 | 0.8606 | 0.9734 |
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- | 0.0778 | 3.36 | 3000 | 0.1178 | 0.8550 | 0.8779 | 0.8663 | 0.9746 |
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- | 0.0696 | 3.92 | 3500 | 0.1168 | 0.8491 | 0.8878 | 0.8680 | 0.9738 |
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- | 0.0565 | 4.48 | 4000 | 0.1135 | 0.8377 | 0.8734 | 0.8552 | 0.9734 |
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- | 0.0532 | 5.04 | 4500 | 0.1218 | 0.8673 | 0.8888 | 0.8779 | 0.9752 |
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- | 0.0451 | 5.6 | 5000 | 0.1339 | 0.8613 | 0.8878 | 0.8744 | 0.9751 |
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- | 0.0396 | 6.16 | 5500 | 0.1339 | 0.8595 | 0.8864 | 0.8727 | 0.9751 |
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- | 0.0331 | 6.72 | 6000 | 0.1361 | 0.8617 | 0.8933 | 0.8772 | 0.9755 |
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- | 0.0263 | 7.28 | 6500 | 0.1450 | 0.8720 | 0.8958 | 0.8837 | 0.9758 |
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- | 0.0278 | 7.84 | 7000 | 0.1456 | 0.8676 | 0.8973 | 0.8822 | 0.9754 |
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  ### Framework versions
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.8860882210373243
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  - name: Recall
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  type: recall
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+ value: 0.9071960297766749
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  - name: F1
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  type: f1
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+ value: 0.8965179009318294
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9772540983606557
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the cnec dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1919
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+ - Precision: 0.8861
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+ - Recall: 0.9072
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+ - F1: 0.8965
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+ - Accuracy: 0.9773
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  ## Model description
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - num_epochs: 15
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.1664 | 1.12 | 1000 | 0.1312 | 0.8299 | 0.8521 | 0.8408 | 0.9695 |
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+ | 0.1153 | 2.24 | 2000 | 0.1121 | 0.8283 | 0.8640 | 0.8458 | 0.9722 |
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+ | 0.0815 | 3.36 | 3000 | 0.1159 | 0.8523 | 0.8531 | 0.8527 | 0.9735 |
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+ | 0.0633 | 4.48 | 4000 | 0.1166 | 0.8515 | 0.8819 | 0.8664 | 0.9750 |
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+ | 0.0472 | 5.6 | 5000 | 0.1624 | 0.8635 | 0.8918 | 0.8774 | 0.9735 |
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+ | 0.0369 | 6.72 | 6000 | 0.1476 | 0.8710 | 0.8983 | 0.8844 | 0.9770 |
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+ | 0.0325 | 7.84 | 7000 | 0.1590 | 0.8710 | 0.8943 | 0.8825 | 0.9752 |
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+ | 0.0268 | 8.96 | 8000 | 0.1698 | 0.8709 | 0.9037 | 0.8870 | 0.9761 |
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+ | 0.0236 | 10.08 | 9000 | 0.1721 | 0.8807 | 0.9087 | 0.8945 | 0.9763 |
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+ | 0.0125 | 11.2 | 10000 | 0.1843 | 0.8781 | 0.9047 | 0.8912 | 0.9768 |
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+ | 0.009 | 12.32 | 11000 | 0.1971 | 0.8789 | 0.9077 | 0.8931 | 0.9766 |
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+ | 0.0097 | 13.44 | 12000 | 0.1823 | 0.8857 | 0.9077 | 0.8966 | 0.9775 |
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+ | 0.0077 | 14.56 | 13000 | 0.1919 | 0.8861 | 0.9072 | 0.8965 | 0.9773 |
 
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  ### Framework versions
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