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CNEC_extended_xlm-roberta-large

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xlm-roberta
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  1. README.md +37 -24
  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: 0.8548310328415041
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  - name: Recall
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  type: recall
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- value: 0.8913151364764268
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  - name: F1
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  type: f1
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- value: 0.8726919339164239
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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
@@ -45,10 +45,10 @@ 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: 0.1540
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- - Precision: 0.8548
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- - Recall: 0.8913
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- - F1: 0.8727
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- - Accuracy: 0.9754
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  ## Model description
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@@ -68,8 +68,8 @@ 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: 8
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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
@@ -77,20 +77,33 @@ The following hyperparameters were used during training:
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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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- | 0.2864 | 0.56 | 500 | 0.1328 | 0.7015 | 0.8119 | 0.7527 | 0.9629 |
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- | 0.13 | 1.12 | 1000 | 0.1221 | 0.7836 | 0.8734 | 0.8261 | 0.9701 |
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- | 0.0972 | 1.68 | 1500 | 0.1140 | 0.7836 | 0.8610 | 0.8205 | 0.9710 |
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- | 0.0807 | 2.24 | 2000 | 0.1244 | 0.8032 | 0.8730 | 0.8366 | 0.9730 |
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- | 0.0626 | 2.8 | 2500 | 0.1135 | 0.8104 | 0.8844 | 0.8458 | 0.9755 |
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- | 0.0451 | 3.36 | 3000 | 0.1371 | 0.8305 | 0.8824 | 0.8556 | 0.9733 |
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- | 0.0397 | 3.92 | 3500 | 0.1251 | 0.8307 | 0.8814 | 0.8553 | 0.9736 |
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- | 0.0244 | 4.48 | 4000 | 0.1441 | 0.8370 | 0.8794 | 0.8577 | 0.9740 |
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- | 0.0257 | 5.04 | 4500 | 0.1319 | 0.8541 | 0.8888 | 0.8711 | 0.9759 |
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- | 0.0164 | 5.6 | 5000 | 0.1465 | 0.8421 | 0.8868 | 0.8639 | 0.9754 |
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- | 0.013 | 6.16 | 5500 | 0.1494 | 0.8473 | 0.8868 | 0.8666 | 0.9751 |
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- | 0.0108 | 6.72 | 6000 | 0.1540 | 0.8548 | 0.8913 | 0.8727 | 0.9754 |
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.8481132075471698
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  - name: Recall
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  type: recall
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+ value: 0.8923076923076924
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  - name: F1
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  type: f1
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+ value: 0.8696493349455865
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9768735362997658
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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: 0.1540
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+ - Precision: 0.8481
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+ - Recall: 0.8923
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+ - F1: 0.8696
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+ - Accuracy: 0.9769
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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: 4
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+ - eval_batch_size: 4
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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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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.3844 | 0.28 | 500 | 0.2098 | 0.6100 | 0.7474 | 0.6717 | 0.9487 |
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+ | 0.2166 | 0.56 | 1000 | 0.1502 | 0.7313 | 0.8065 | 0.7671 | 0.9618 |
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+ | 0.1712 | 0.84 | 1500 | 0.1321 | 0.7447 | 0.8427 | 0.7907 | 0.9653 |
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+ | 0.1646 | 1.12 | 2000 | 0.1227 | 0.7516 | 0.8422 | 0.7943 | 0.9681 |
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+ | 0.1336 | 1.4 | 2500 | 0.1233 | 0.7729 | 0.8447 | 0.8072 | 0.9688 |
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+ | 0.1212 | 1.68 | 3000 | 0.1308 | 0.7989 | 0.8655 | 0.8309 | 0.9714 |
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+ | 0.1268 | 1.96 | 3500 | 0.1298 | 0.7867 | 0.8660 | 0.8245 | 0.9718 |
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+ | 0.0979 | 2.24 | 4000 | 0.1142 | 0.8111 | 0.8844 | 0.8462 | 0.9740 |
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+ | 0.1 | 2.52 | 4500 | 0.1316 | 0.8159 | 0.8799 | 0.8467 | 0.9724 |
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+ | 0.0971 | 2.8 | 5000 | 0.1334 | 0.8228 | 0.8849 | 0.8527 | 0.9737 |
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+ | 0.0912 | 3.08 | 5500 | 0.1348 | 0.8277 | 0.8844 | 0.8551 | 0.9755 |
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+ | 0.0661 | 3.36 | 6000 | 0.1349 | 0.8213 | 0.8849 | 0.8519 | 0.9747 |
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+ | 0.0672 | 3.64 | 6500 | 0.1423 | 0.8301 | 0.8898 | 0.8589 | 0.9735 |
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+ | 0.0721 | 3.92 | 7000 | 0.1242 | 0.8402 | 0.8923 | 0.8655 | 0.9764 |
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+ | 0.0703 | 4.2 | 7500 | 0.1351 | 0.8204 | 0.8794 | 0.8489 | 0.9737 |
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+ | 0.0503 | 4.48 | 8000 | 0.1625 | 0.8273 | 0.8918 | 0.8584 | 0.9747 |
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+ | 0.054 | 4.76 | 8500 | 0.1556 | 0.8276 | 0.8839 | 0.8548 | 0.9745 |
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+ | 0.0452 | 5.04 | 9000 | 0.1454 | 0.8360 | 0.8903 | 0.8623 | 0.9756 |
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+ | 0.0392 | 5.32 | 9500 | 0.1548 | 0.8406 | 0.8923 | 0.8657 | 0.9769 |
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+ | 0.0357 | 5.6 | 10000 | 0.1473 | 0.8446 | 0.8953 | 0.8692 | 0.9770 |
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+ | 0.0389 | 5.88 | 10500 | 0.1463 | 0.8494 | 0.8983 | 0.8731 | 0.9768 |
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+ | 0.0331 | 6.16 | 11000 | 0.1530 | 0.8503 | 0.8938 | 0.8715 | 0.9769 |
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+ | 0.0273 | 6.44 | 11500 | 0.1553 | 0.8483 | 0.8933 | 0.8702 | 0.9770 |
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+ | 0.0315 | 6.72 | 12000 | 0.1537 | 0.8499 | 0.8938 | 0.8713 | 0.9768 |
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+ | 0.0274 | 7.0 | 12500 | 0.1540 | 0.8481 | 0.8923 | 0.8696 | 0.9769 |
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  ### Framework versions
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