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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 +26 -21
  2. model.safetensors +1 -1
  3. runs/Mar06_16-20-16_n32/events.out.tfevents.1709738419.n32.1455937.1 +2 -2
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: 0.8280399274047187
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  - name: Recall
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  type: recall
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- value: 0.8807915057915058
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  - name: F1
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  type: f1
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- value: 0.8536014967259121
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  - name: Accuracy
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  type: accuracy
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- value: 0.9694915254237289
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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: 0.1664
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- - Precision: 0.8280
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- - Recall: 0.8808
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- - F1: 0.8536
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- - Accuracy: 0.9695
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  ## Model description
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@@ -68,26 +68,31 @@ More information needed
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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: 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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- | 0.3183 | 1.11 | 500 | 0.1528 | 0.6999 | 0.7934 | 0.7437 | 0.9583 |
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- | 0.144 | 2.22 | 1000 | 0.1302 | 0.7521 | 0.8639 | 0.8041 | 0.9648 |
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- | 0.1015 | 3.33 | 1500 | 0.1431 | 0.8003 | 0.8721 | 0.8346 | 0.9678 |
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- | 0.0807 | 4.44 | 2000 | 0.1355 | 0.7840 | 0.8740 | 0.8266 | 0.9680 |
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- | 0.066 | 5.56 | 2500 | 0.1413 | 0.8196 | 0.8793 | 0.8484 | 0.9691 |
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- | 0.0492 | 6.67 | 3000 | 0.1461 | 0.8132 | 0.8803 | 0.8454 | 0.9700 |
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- | 0.0401 | 7.78 | 3500 | 0.1577 | 0.8229 | 0.8769 | 0.8491 | 0.9690 |
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- | 0.0312 | 8.89 | 4000 | 0.1637 | 0.8242 | 0.8822 | 0.8522 | 0.9700 |
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- | 0.0265 | 10.0 | 4500 | 0.1664 | 0.8280 | 0.8808 | 0.8536 | 0.9695 |
 
 
 
 
 
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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: 0.8299725022914757
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  - name: Recall
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  type: recall
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+ value: 0.874034749034749
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  - name: F1
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  type: f1
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+ value: 0.8514339445228021
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9687092568448501
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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: 0.1727
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+ - Precision: 0.8300
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+ - Recall: 0.8740
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+ - F1: 0.8514
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+ - Accuracy: 0.9687
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  ## Model description
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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: 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: 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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+ | 0.3664 | 0.56 | 500 | 0.1708 | 0.6886 | 0.8132 | 0.7457 | 0.9536 |
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+ | 0.1841 | 1.11 | 1000 | 0.1512 | 0.7474 | 0.8470 | 0.7941 | 0.9631 |
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+ | 0.1528 | 1.67 | 1500 | 0.1650 | 0.7530 | 0.8181 | 0.7842 | 0.9612 |
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+ | 0.1313 | 2.22 | 2000 | 0.1598 | 0.7809 | 0.8687 | 0.8225 | 0.9656 |
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+ | 0.1094 | 2.78 | 2500 | 0.1421 | 0.7791 | 0.8475 | 0.8118 | 0.9636 |
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+ | 0.0897 | 3.33 | 3000 | 0.1395 | 0.7958 | 0.8634 | 0.8282 | 0.9669 |
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+ | 0.0864 | 3.89 | 3500 | 0.1454 | 0.7897 | 0.8789 | 0.8319 | 0.9664 |
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+ | 0.0674 | 4.44 | 4000 | 0.1524 | 0.8174 | 0.8663 | 0.8411 | 0.9675 |
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+ | 0.0689 | 5.0 | 4500 | 0.1475 | 0.8178 | 0.8687 | 0.8425 | 0.9674 |
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+ | 0.05 | 5.56 | 5000 | 0.1628 | 0.8257 | 0.8731 | 0.8487 | 0.9676 |
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+ | 0.0521 | 6.11 | 5500 | 0.1614 | 0.8257 | 0.8644 | 0.8446 | 0.9668 |
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+ | 0.0409 | 6.67 | 6000 | 0.1648 | 0.8258 | 0.8740 | 0.8492 | 0.9681 |
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+ | 0.0345 | 7.22 | 6500 | 0.1684 | 0.8295 | 0.8711 | 0.8498 | 0.9682 |
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+ | 0.0302 | 7.78 | 7000 | 0.1727 | 0.8300 | 0.8740 | 0.8514 | 0.9687 |
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  ### Framework versions
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