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stulcrad
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CNEC2_0_Supertypes_xlm-roberta-large

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xlm-roberta
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  1. README.md +27 -22
  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: 0.8675762439807384
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  - name: Recall
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  type: recall
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- value: 0.8930194134655102
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  - name: F1
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  type: f1
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- value: 0.880113983309587
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  - name: Accuracy
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  type: accuracy
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- value: 0.9709981167608286
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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: 0.1744
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- - Precision: 0.8676
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- - Recall: 0.8930
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- - F1: 0.8801
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- - Accuracy: 0.9710
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  ## Model description
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@@ -73,22 +73,27 @@ 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: 10
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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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- | 0.4736 | 1.0 | 900 | 0.1585 | 0.7678 | 0.8319 | 0.7986 | 0.9597 |
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- | 0.1665 | 2.0 | 1800 | 0.1418 | 0.8237 | 0.8550 | 0.8391 | 0.9650 |
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- | 0.129 | 3.0 | 2700 | 0.1361 | 0.8299 | 0.8686 | 0.8488 | 0.9682 |
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- | 0.0998 | 4.0 | 3600 | 0.1322 | 0.8474 | 0.8852 | 0.8659 | 0.9698 |
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- | 0.0867 | 5.0 | 4500 | 0.1479 | 0.8419 | 0.8823 | 0.8616 | 0.9704 |
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- | 0.0709 | 6.0 | 5400 | 0.1418 | 0.8539 | 0.8815 | 0.8675 | 0.9708 |
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- | 0.0635 | 7.0 | 6300 | 0.1579 | 0.8626 | 0.8819 | 0.8721 | 0.9704 |
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- | 0.0512 | 8.0 | 7200 | 0.1624 | 0.8649 | 0.8910 | 0.8777 | 0.9704 |
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- | 0.0444 | 9.0 | 8100 | 0.1670 | 0.8702 | 0.8914 | 0.8806 | 0.9712 |
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- | 0.0399 | 10.0 | 9000 | 0.1744 | 0.8676 | 0.8930 | 0.8801 | 0.9710 |
 
 
 
 
 
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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: 0.8564668769716088
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  - name: Recall
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  type: recall
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+ value: 0.8971499380421314
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  - name: F1
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  type: f1
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+ value: 0.876336493847085
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9708532522091844
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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: 0.2155
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+ - Precision: 0.8565
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+ - Recall: 0.8971
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+ - F1: 0.8763
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+ - Accuracy: 0.9709
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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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+ | 0.4393 | 1.0 | 900 | 0.1671 | 0.7756 | 0.8195 | 0.7969 | 0.9590 |
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+ | 0.1716 | 2.0 | 1800 | 0.1409 | 0.8155 | 0.8583 | 0.8364 | 0.9662 |
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+ | 0.1326 | 3.0 | 2700 | 0.1288 | 0.8203 | 0.8748 | 0.8467 | 0.9687 |
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+ | 0.1027 | 4.0 | 3600 | 0.1408 | 0.8290 | 0.8732 | 0.8505 | 0.9683 |
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+ | 0.0891 | 5.0 | 4500 | 0.1447 | 0.8485 | 0.9000 | 0.8735 | 0.9725 |
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+ | 0.0715 | 6.0 | 5400 | 0.1393 | 0.8561 | 0.8868 | 0.8712 | 0.9713 |
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+ | 0.0644 | 7.0 | 6300 | 0.1586 | 0.8517 | 0.8918 | 0.8713 | 0.9702 |
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+ | 0.0535 | 8.0 | 7200 | 0.1526 | 0.8481 | 0.8810 | 0.8643 | 0.9696 |
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+ | 0.0492 | 9.0 | 8100 | 0.1795 | 0.8529 | 0.8984 | 0.8751 | 0.9702 |
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+ | 0.0391 | 10.0 | 9000 | 0.1903 | 0.8536 | 0.8938 | 0.8733 | 0.9693 |
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+ | 0.0323 | 11.0 | 9900 | 0.1885 | 0.8615 | 0.9046 | 0.8825 | 0.9724 |
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+ | 0.0274 | 12.0 | 10800 | 0.2099 | 0.8585 | 0.9025 | 0.8800 | 0.9696 |
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+ | 0.0237 | 13.0 | 11700 | 0.1944 | 0.8624 | 0.9009 | 0.8812 | 0.9720 |
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+ | 0.0245 | 14.0 | 12600 | 0.2129 | 0.8618 | 0.8967 | 0.8789 | 0.9711 |
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+ | 0.0206 | 15.0 | 13500 | 0.2155 | 0.8565 | 0.8971 | 0.8763 | 0.9709 |
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  ### Framework versions
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