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stulcrad
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CNEC1_1_extended_xlm-roberta-large

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xlm-roberta
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  1. README.md +21 -24
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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: 0.8424273329933707
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  - name: Recall
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  type: recall
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- value: 0.882950293960449
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  - name: F1
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  type: f1
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- value: 0.8622129436325678
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  - name: Accuracy
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  type: accuracy
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- value: 0.9652851996991648
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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: 0.2119
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- - Precision: 0.8424
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- - Recall: 0.8830
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- - F1: 0.8622
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- - Accuracy: 0.9653
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  ## Model description
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@@ -67,29 +67,26 @@ 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: 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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  - 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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- | 0.3746 | 0.86 | 500 | 0.1861 | 0.7228 | 0.8097 | 0.7638 | 0.9523 |
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- | 0.2127 | 1.72 | 1000 | 0.1635 | 0.7829 | 0.8461 | 0.8133 | 0.9611 |
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- | 0.1494 | 2.58 | 1500 | 0.1704 | 0.7579 | 0.8466 | 0.7998 | 0.9546 |
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- | 0.1274 | 3.44 | 2000 | 0.1800 | 0.8003 | 0.8675 | 0.8325 | 0.9615 |
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- | 0.0987 | 4.3 | 2500 | 0.1511 | 0.8025 | 0.8883 | 0.8432 | 0.9657 |
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- | 0.0827 | 5.16 | 3000 | 0.1910 | 0.8179 | 0.8739 | 0.8450 | 0.9630 |
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- | 0.0677 | 6.02 | 3500 | 0.1655 | 0.8374 | 0.8808 | 0.8586 | 0.9689 |
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- | 0.0475 | 6.88 | 4000 | 0.1793 | 0.8270 | 0.8658 | 0.8460 | 0.9633 |
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- | 0.0396 | 7.75 | 4500 | 0.1687 | 0.8363 | 0.8899 | 0.8622 | 0.9672 |
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- | 0.0256 | 8.61 | 5000 | 0.1904 | 0.8315 | 0.8808 | 0.8554 | 0.9665 |
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- | 0.0223 | 9.47 | 5500 | 0.2119 | 0.8424 | 0.8830 | 0.8622 | 0.9653 |
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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: 0.8551829268292683
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  - name: Recall
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  type: recall
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+ value: 0.8995189738107964
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  - name: F1
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  type: f1
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+ value: 0.8767908309455589
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9694414756758897
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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: 0.2115
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+ - Precision: 0.8552
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+ - Recall: 0.8995
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+ - F1: 0.8768
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+ - Accuracy: 0.9694
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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: 2e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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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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+ | 0.2948 | 1.72 | 500 | 0.1385 | 0.7752 | 0.8589 | 0.8149 | 0.9620 |
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+ | 0.1185 | 3.44 | 1000 | 0.1411 | 0.8063 | 0.8808 | 0.8419 | 0.9692 |
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+ | 0.0762 | 5.15 | 1500 | 0.1485 | 0.8252 | 0.8781 | 0.8509 | 0.9690 |
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+ | 0.054 | 6.87 | 2000 | 0.1586 | 0.8368 | 0.8878 | 0.8615 | 0.9697 |
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+ | 0.0357 | 8.59 | 2500 | 0.1774 | 0.8364 | 0.8990 | 0.8666 | 0.9705 |
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+ | 0.026 | 10.31 | 3000 | 0.1869 | 0.8540 | 0.8974 | 0.8752 | 0.9700 |
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+ | 0.0189 | 12.03 | 3500 | 0.2040 | 0.8555 | 0.8958 | 0.8752 | 0.9698 |
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+ | 0.013 | 13.75 | 4000 | 0.2115 | 0.8552 | 0.8995 | 0.8768 | 0.9694 |
 
 
 
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  ### Framework versions
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