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stulcrad
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CNEC1_1_Supertypes_xlm-roberta-large

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xlm-roberta
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  1. README.md +21 -26
  2. model.safetensors +1 -1
  3. runs/Mar06_15-53-37_n28/events.out.tfevents.1709736818.n28.863430.1 +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: 0.8374155405405406
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  - name: Recall
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  type: recall
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- value: 0.8896366083445492
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  - name: F1
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  type: f1
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- value: 0.8627365673265174
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  - name: Accuracy
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  type: accuracy
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- value: 0.9609274366680979
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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: 0.2870
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- - Precision: 0.8374
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- - Recall: 0.8896
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- - F1: 0.8627
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- - Accuracy: 0.9609
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  ## Model description
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@@ -68,31 +68,26 @@ 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: 16
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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
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- - num_epochs: 25
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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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- | 0.4362 | 1.7 | 500 | 0.1915 | 0.7142 | 0.8407 | 0.7723 | 0.9498 |
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- | 0.1873 | 3.4 | 1000 | 0.1735 | 0.7945 | 0.8793 | 0.8348 | 0.9584 |
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- | 0.1395 | 5.1 | 1500 | 0.1774 | 0.7771 | 0.8681 | 0.8201 | 0.9582 |
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- | 0.1031 | 6.8 | 2000 | 0.1837 | 0.8025 | 0.8748 | 0.8371 | 0.9582 |
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- | 0.0825 | 8.5 | 2500 | 0.1937 | 0.8106 | 0.8852 | 0.8462 | 0.9585 |
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- | 0.0671 | 10.2 | 3000 | 0.2007 | 0.8338 | 0.8932 | 0.8625 | 0.9609 |
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- | 0.0538 | 11.9 | 3500 | 0.2101 | 0.8222 | 0.8901 | 0.8548 | 0.9603 |
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- | 0.0419 | 13.61 | 4000 | 0.2177 | 0.8186 | 0.8905 | 0.8530 | 0.9619 |
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- | 0.0361 | 15.31 | 4500 | 0.2299 | 0.8316 | 0.8843 | 0.8571 | 0.9612 |
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- | 0.0281 | 17.01 | 5000 | 0.2474 | 0.8300 | 0.8825 | 0.8554 | 0.9610 |
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- | 0.0234 | 18.71 | 5500 | 0.2623 | 0.8327 | 0.8843 | 0.8577 | 0.9606 |
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- | 0.0194 | 20.41 | 6000 | 0.2702 | 0.8311 | 0.8829 | 0.8562 | 0.9603 |
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- | 0.0169 | 22.11 | 6500 | 0.2781 | 0.8358 | 0.8883 | 0.8612 | 0.9608 |
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- | 0.0151 | 23.81 | 7000 | 0.2870 | 0.8374 | 0.8896 | 0.8627 | 0.9609 |
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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: 0.8205234732031574
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  - name: Recall
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  type: recall
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+ value: 0.88604755495738
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  - name: F1
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  type: f1
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+ value: 0.8520276100086281
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9624497443303798
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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: 0.1914
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+ - Precision: 0.8205
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+ - Recall: 0.8860
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+ - F1: 0.8520
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+ - Accuracy: 0.9624
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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: 8
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+ - eval_batch_size: 8
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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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+ | 0.4532 | 0.85 | 500 | 0.2036 | 0.7303 | 0.8295 | 0.7767 | 0.9476 |
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+ | 0.2172 | 1.7 | 1000 | 0.1727 | 0.7560 | 0.8591 | 0.8043 | 0.9566 |
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+ | 0.1572 | 2.56 | 1500 | 0.1901 | 0.7733 | 0.8690 | 0.8183 | 0.9566 |
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+ | 0.1341 | 3.41 | 2000 | 0.1661 | 0.7905 | 0.8753 | 0.8307 | 0.9599 |
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+ | 0.1093 | 4.26 | 2500 | 0.1747 | 0.8087 | 0.8856 | 0.8454 | 0.9610 |
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+ | 0.0876 | 5.11 | 3000 | 0.1987 | 0.7949 | 0.8798 | 0.8352 | 0.9588 |
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+ | 0.0752 | 5.96 | 3500 | 0.1827 | 0.8146 | 0.8834 | 0.8476 | 0.9622 |
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+ | 0.0574 | 6.81 | 4000 | 0.1834 | 0.8221 | 0.8937 | 0.8564 | 0.9638 |
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+ | 0.0542 | 7.67 | 4500 | 0.1914 | 0.8205 | 0.8860 | 0.8520 | 0.9624 |
 
 
 
 
 
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  ### Framework versions
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