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stulcrad
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CNEC1_1_xlm-roberta-large

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xlm-roberta
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  1. README.md +19 -21
  2. model.safetensors +1 -1
  3. runs/Mar06_15-53-24_n21/events.out.tfevents.1709736805.n21.3385783.1 +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: 0.8387096774193549
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  - name: Recall
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  type: recall
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- value: 0.8838852097130243
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  - name: F1
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  type: f1
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- value: 0.8607050730868444
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  - name: Accuracy
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  type: accuracy
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- value: 0.9616690737343377
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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: 0.2514
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- - Precision: 0.8387
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- - Recall: 0.8839
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- - F1: 0.8607
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- - Accuracy: 0.9617
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  ## Model description
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@@ -73,23 +73,21 @@ 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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- | 0.5884 | 0.85 | 500 | 0.2401 | 0.7095 | 0.8022 | 0.7530 | 0.9450 |
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- | 0.2568 | 1.7 | 1000 | 0.2119 | 0.7630 | 0.8313 | 0.7957 | 0.9510 |
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- | 0.1871 | 2.56 | 1500 | 0.2000 | 0.7857 | 0.8305 | 0.8075 | 0.9534 |
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- | 0.1475 | 3.41 | 2000 | 0.1958 | 0.7763 | 0.8547 | 0.8136 | 0.9554 |
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- | 0.1231 | 4.26 | 2500 | 0.1978 | 0.7972 | 0.8711 | 0.8325 | 0.9562 |
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- | 0.091 | 5.11 | 3000 | 0.2219 | 0.8126 | 0.8808 | 0.8453 | 0.9587 |
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- | 0.068 | 5.96 | 3500 | 0.2162 | 0.8231 | 0.8728 | 0.8472 | 0.9584 |
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- | 0.0535 | 6.81 | 4000 | 0.2368 | 0.8351 | 0.8808 | 0.8573 | 0.9607 |
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- | 0.0463 | 7.67 | 4500 | 0.2442 | 0.8330 | 0.8786 | 0.8552 | 0.9598 |
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- | 0.0326 | 8.52 | 5000 | 0.2495 | 0.8359 | 0.8790 | 0.8569 | 0.9605 |
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- | 0.0286 | 9.37 | 5500 | 0.2514 | 0.8387 | 0.8839 | 0.8607 | 0.9617 |
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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: 0.8197264815582262
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  - name: Recall
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  type: recall
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+ value: 0.873289183222958
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  - name: F1
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  type: f1
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+ value: 0.8456605386917486
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9604980678402748
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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: 0.8197
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+ - Recall: 0.8733
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+ - F1: 0.8457
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+ - Accuracy: 0.9605
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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: 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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+ | 0.7506 | 0.85 | 500 | 0.2818 | 0.6550 | 0.7687 | 0.7073 | 0.9354 |
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+ | 0.2862 | 1.7 | 1000 | 0.2055 | 0.7555 | 0.8238 | 0.7882 | 0.9500 |
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+ | 0.2057 | 2.56 | 1500 | 0.2090 | 0.7792 | 0.8415 | 0.8092 | 0.9534 |
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+ | 0.1698 | 3.41 | 2000 | 0.1992 | 0.7818 | 0.8623 | 0.8201 | 0.9575 |
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+ | 0.1366 | 4.26 | 2500 | 0.2036 | 0.8086 | 0.8746 | 0.8403 | 0.9584 |
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+ | 0.1049 | 5.11 | 3000 | 0.2000 | 0.8062 | 0.8689 | 0.8364 | 0.9607 |
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+ | 0.0885 | 5.96 | 3500 | 0.2087 | 0.8059 | 0.8689 | 0.8362 | 0.9571 |
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+ | 0.0673 | 6.81 | 4000 | 0.2063 | 0.8281 | 0.8786 | 0.8526 | 0.9602 |
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+ | 0.0628 | 7.67 | 4500 | 0.2155 | 0.8197 | 0.8733 | 0.8457 | 0.9605 |
 
 
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  ### Framework versions
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