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stulcrad
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CNEC_extended_xlm-roberta-large

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xlm-roberta
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  1. README.md +21 -21
  2. model.safetensors +1 -1
  3. runs/Feb26_16-27-31_n29/events.out.tfevents.1708961253.n29.26646.2 +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: 0.8493919550982226
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  - name: Recall
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  type: recall
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- value: 0.9012406947890819
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  - name: F1
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  type: f1
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- value: 0.8745485191427884
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  - name: Accuracy
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  type: accuracy
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- value: 0.977195550351288
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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: 0.1518
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- - Precision: 0.8494
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- - Recall: 0.9012
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- - F1: 0.8745
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- - Accuracy: 0.9772
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  ## Model description
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@@ -79,18 +79,18 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 0.3244 | 1.12 | 500 | 0.1364 | 0.7215 | 0.8462 | 0.7789 | 0.9654 |
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- | 0.1375 | 2.24 | 1000 | 0.1184 | 0.7865 | 0.8610 | 0.8221 | 0.9723 |
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- | 0.097 | 3.36 | 1500 | 0.1156 | 0.7943 | 0.8680 | 0.8295 | 0.9737 |
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- | 0.0754 | 4.47 | 2000 | 0.1192 | 0.7978 | 0.8794 | 0.8366 | 0.9738 |
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- | 0.0619 | 5.59 | 2500 | 0.1185 | 0.8168 | 0.8849 | 0.8495 | 0.9751 |
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- | 0.0524 | 6.71 | 3000 | 0.1291 | 0.8237 | 0.8834 | 0.8525 | 0.9752 |
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- | 0.0417 | 7.83 | 3500 | 0.1251 | 0.8405 | 0.8968 | 0.8677 | 0.9776 |
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- | 0.0342 | 8.95 | 4000 | 0.1197 | 0.8280 | 0.8913 | 0.8585 | 0.9771 |
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- | 0.0274 | 10.07 | 4500 | 0.1434 | 0.8415 | 0.8983 | 0.8689 | 0.9762 |
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- | 0.0233 | 11.19 | 5000 | 0.1527 | 0.8547 | 0.8993 | 0.8764 | 0.9757 |
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- | 0.0196 | 12.3 | 5500 | 0.1566 | 0.8531 | 0.9022 | 0.8770 | 0.9766 |
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- | 0.0169 | 13.42 | 6000 | 0.1518 | 0.8494 | 0.9012 | 0.8745 | 0.9772 |
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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: 0.8495533615420781
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  - name: Recall
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  type: recall
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+ value: 0.896774193548387
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  - name: F1
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  type: f1
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+ value: 0.8725253500724288
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9753512880562061
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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: 0.1667
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+ - Precision: 0.8496
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+ - Recall: 0.8968
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+ - F1: 0.8725
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+ - Accuracy: 0.9754
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.4608 | 1.12 | 500 | 0.1709 | 0.6984 | 0.8089 | 0.7496 | 0.9579 |
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+ | 0.1492 | 2.24 | 1000 | 0.1188 | 0.7702 | 0.8467 | 0.8066 | 0.9690 |
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+ | 0.0993 | 3.36 | 1500 | 0.1077 | 0.8123 | 0.8804 | 0.8450 | 0.9739 |
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+ | 0.0724 | 4.47 | 2000 | 0.1113 | 0.8156 | 0.8824 | 0.8477 | 0.9759 |
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+ | 0.0603 | 5.59 | 2500 | 0.1337 | 0.8234 | 0.8864 | 0.8537 | 0.9739 |
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+ | 0.045 | 6.71 | 3000 | 0.1386 | 0.8446 | 0.8958 | 0.8695 | 0.9756 |
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+ | 0.0358 | 7.83 | 3500 | 0.1371 | 0.8449 | 0.8978 | 0.8705 | 0.9756 |
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+ | 0.0284 | 8.95 | 4000 | 0.1364 | 0.8390 | 0.8998 | 0.8683 | 0.9756 |
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+ | 0.0229 | 10.07 | 4500 | 0.1479 | 0.8328 | 0.8873 | 0.8592 | 0.9734 |
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+ | 0.0172 | 11.19 | 5000 | 0.1658 | 0.8446 | 0.8958 | 0.8695 | 0.9754 |
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+ | 0.0146 | 12.3 | 5500 | 0.1650 | 0.8448 | 0.8968 | 0.8700 | 0.9749 |
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+ | 0.0126 | 13.42 | 6000 | 0.1667 | 0.8496 | 0.8968 | 0.8725 | 0.9754 |
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  ### Framework versions
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