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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 +14 -13
  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: 0.8366164542294322
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  - name: Recall
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  type: recall
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- value: 0.8946716232961586
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  - name: F1
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  type: f1
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- value: 0.8646706586826347
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  - name: Accuracy
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  type: accuracy
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- value: 0.9697812545270172
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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: 0.1777
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- - Precision: 0.8366
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- - Recall: 0.8947
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- - F1: 0.8647
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- - Accuracy: 0.9698
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  ## Model description
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@@ -73,15 +73,16 @@ 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: 3
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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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- | 0.1989 | 1.0 | 7193 | 0.1998 | 0.7372 | 0.8249 | 0.7786 | 0.9575 |
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- | 0.1398 | 2.0 | 14386 | 0.1890 | 0.8257 | 0.8827 | 0.8533 | 0.9662 |
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- | 0.0926 | 3.0 | 21579 | 0.1777 | 0.8366 | 0.8947 | 0.8647 | 0.9698 |
 
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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: 0.8574273197929112
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  - name: Recall
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  type: recall
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+ value: 0.889301941346551
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  - name: F1
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  type: f1
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+ value: 0.8730738037307381
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9718673040706939
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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: 0.1905
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+ - Precision: 0.8574
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+ - Recall: 0.8893
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+ - F1: 0.8731
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+ - Accuracy: 0.9719
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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: 4
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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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+ | 0.2136 | 1.0 | 7193 | 0.1833 | 0.7605 | 0.8513 | 0.8034 | 0.9620 |
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+ | 0.1556 | 2.0 | 14386 | 0.1683 | 0.8282 | 0.8881 | 0.8571 | 0.9689 |
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+ | 0.1154 | 3.0 | 21579 | 0.1599 | 0.8409 | 0.8819 | 0.8609 | 0.9703 |
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+ | 0.0522 | 4.0 | 28772 | 0.1905 | 0.8574 | 0.8893 | 0.8731 | 0.9719 |
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  ### Framework versions
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