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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 +15 -13
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@@ -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: 0.8447596532702916
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  - name: Recall
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  type: recall
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- value: 0.8855844692275919
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  - name: F1
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  type: f1
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- value: 0.8646904617866505
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  - name: Accuracy
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  type: accuracy
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- value: 0.9681587715486021
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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: 0.1762
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- - Precision: 0.8448
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- - Recall: 0.8856
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- - F1: 0.8647
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- - Accuracy: 0.9682
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  ## Model description
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@@ -73,15 +73,17 @@ 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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- | 0.2106 | 1.0 | 7193 | 0.2086 | 0.7859 | 0.8203 | 0.8027 | 0.9563 |
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- | 0.1136 | 2.0 | 14386 | 0.1710 | 0.8391 | 0.8678 | 0.8532 | 0.9658 |
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- | 0.0973 | 3.0 | 21579 | 0.1762 | 0.8448 | 0.8856 | 0.8647 | 0.9682 |
 
 
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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: 0.8515562649640862
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  - name: Recall
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  type: recall
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+ value: 0.8814539446509707
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  - name: F1
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  type: f1
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+ value: 0.8662472092551248
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9700709836303056
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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: 0.2179
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+ - Precision: 0.8516
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+ - Recall: 0.8815
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+ - F1: 0.8662
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+ - Accuracy: 0.9701
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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: 5
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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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+ | 0.267 | 1.0 | 7193 | 0.2806 | 0.7707 | 0.8009 | 0.7855 | 0.9525 |
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+ | 0.1977 | 2.0 | 14386 | 0.1792 | 0.8151 | 0.8451 | 0.8299 | 0.9616 |
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+ | 0.1767 | 3.0 | 21579 | 0.1935 | 0.8293 | 0.8711 | 0.8497 | 0.9662 |
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+ | 0.0929 | 4.0 | 28772 | 0.2219 | 0.8382 | 0.8860 | 0.8614 | 0.9677 |
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+ | 0.0788 | 5.0 | 35965 | 0.2179 | 0.8516 | 0.8815 | 0.8662 | 0.9701 |
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  ### Framework versions