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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 +26 -26
  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: 0.8296228986824171
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  - name: Recall
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  type: recall
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  value: 0.8812741312741312
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  - name: F1
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  type: f1
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- value: 0.8546688509244091
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  - name: Accuracy
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  type: accuracy
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- value: 0.9687672026655078
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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: 0.1851
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- - Precision: 0.8296
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  - Recall: 0.8813
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- - F1: 0.8547
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- - Accuracy: 0.9688
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  ## Model description
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@@ -74,31 +74,31 @@ The following hyperparameters were used during training:
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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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  - lr_scheduler_warmup_ratio: 0.01
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- - lr_scheduler_warmup_steps: 100
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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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- | 0.607 | 0.56 | 500 | 0.1967 | 0.6391 | 0.7881 | 0.7059 | 0.9475 |
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- | 0.2143 | 1.11 | 1000 | 0.1735 | 0.6993 | 0.8282 | 0.7583 | 0.9591 |
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- | 0.1756 | 1.67 | 1500 | 0.1550 | 0.7363 | 0.8016 | 0.7676 | 0.9605 |
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- | 0.1458 | 2.22 | 2000 | 0.1674 | 0.7549 | 0.8591 | 0.8036 | 0.9624 |
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- | 0.1323 | 2.78 | 2500 | 0.1575 | 0.7802 | 0.8460 | 0.8118 | 0.9619 |
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- | 0.112 | 3.33 | 3000 | 0.1627 | 0.7602 | 0.8369 | 0.7967 | 0.9625 |
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- | 0.1082 | 3.89 | 3500 | 0.1510 | 0.7774 | 0.8697 | 0.8210 | 0.9662 |
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- | 0.0862 | 4.44 | 4000 | 0.1544 | 0.7820 | 0.8552 | 0.8170 | 0.9655 |
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- | 0.0878 | 5.0 | 4500 | 0.1399 | 0.8044 | 0.8692 | 0.8355 | 0.9679 |
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- | 0.0657 | 5.56 | 5000 | 0.1518 | 0.8038 | 0.8639 | 0.8328 | 0.9671 |
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- | 0.0741 | 6.11 | 5500 | 0.1704 | 0.8031 | 0.8562 | 0.8288 | 0.9664 |
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- | 0.0575 | 6.67 | 6000 | 0.1774 | 0.8109 | 0.8649 | 0.8370 | 0.9664 |
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- | 0.0497 | 7.22 | 6500 | 0.1801 | 0.8129 | 0.8682 | 0.8397 | 0.9667 |
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- | 0.0419 | 7.78 | 7000 | 0.1659 | 0.8337 | 0.8687 | 0.8509 | 0.9692 |
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- | 0.0445 | 8.33 | 7500 | 0.1752 | 0.8340 | 0.8755 | 0.8543 | 0.9687 |
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- | 0.037 | 8.89 | 8000 | 0.1823 | 0.8213 | 0.8764 | 0.8480 | 0.9680 |
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- | 0.0324 | 9.44 | 8500 | 0.1854 | 0.8268 | 0.8798 | 0.8525 | 0.9686 |
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- | 0.0313 | 10.0 | 9000 | 0.1851 | 0.8296 | 0.8813 | 0.8547 | 0.9688 |
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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: 0.8311333636777424
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  - name: Recall
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  type: recall
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  value: 0.8812741312741312
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  - name: F1
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  type: f1
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+ value: 0.8554696650269384
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9682167173692597
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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: 0.1827
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+ - Precision: 0.8311
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  - Recall: 0.8813
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+ - F1: 0.8555
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+ - Accuracy: 0.9682
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  ## Model description
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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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  - lr_scheduler_warmup_ratio: 0.01
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+ - lr_scheduler_warmup_steps: 1000
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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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+ | 1.0633 | 0.56 | 500 | 0.2514 | 0.4967 | 0.6544 | 0.5648 | 0.9322 |
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+ | 0.2454 | 1.11 | 1000 | 0.2044 | 0.6504 | 0.7901 | 0.7134 | 0.9532 |
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+ | 0.2064 | 1.67 | 1500 | 0.1721 | 0.7254 | 0.7828 | 0.7530 | 0.9562 |
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+ | 0.1698 | 2.22 | 2000 | 0.1755 | 0.7472 | 0.8388 | 0.7904 | 0.9604 |
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+ | 0.1472 | 2.78 | 2500 | 0.1478 | 0.7547 | 0.8417 | 0.7958 | 0.9624 |
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+ | 0.1244 | 3.33 | 3000 | 0.1516 | 0.7934 | 0.8412 | 0.8166 | 0.9638 |
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+ | 0.12 | 3.89 | 3500 | 0.1366 | 0.7851 | 0.8692 | 0.8250 | 0.9665 |
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+ | 0.0946 | 4.44 | 4000 | 0.1678 | 0.7815 | 0.8494 | 0.8141 | 0.9652 |
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+ | 0.1024 | 5.0 | 4500 | 0.1389 | 0.7756 | 0.8509 | 0.8115 | 0.9649 |
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+ | 0.0765 | 5.56 | 5000 | 0.1563 | 0.7824 | 0.8571 | 0.8181 | 0.9663 |
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+ | 0.0802 | 6.11 | 5500 | 0.1677 | 0.8024 | 0.8586 | 0.8296 | 0.9646 |
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+ | 0.0612 | 6.67 | 6000 | 0.1723 | 0.8068 | 0.8769 | 0.8404 | 0.9662 |
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+ | 0.0529 | 7.22 | 6500 | 0.1698 | 0.8230 | 0.8774 | 0.8493 | 0.9686 |
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+ | 0.0476 | 7.78 | 7000 | 0.1648 | 0.8271 | 0.8702 | 0.8481 | 0.9689 |
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+ | 0.0487 | 8.33 | 7500 | 0.1721 | 0.8287 | 0.8707 | 0.8491 | 0.9683 |
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+ | 0.0392 | 8.89 | 8000 | 0.1787 | 0.8222 | 0.8769 | 0.8487 | 0.9681 |
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+ | 0.0361 | 9.44 | 8500 | 0.1803 | 0.8392 | 0.8818 | 0.8600 | 0.9682 |
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+ | 0.034 | 10.0 | 9000 | 0.1827 | 0.8311 | 0.8813 | 0.8555 | 0.9682 |
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  ### Framework versions
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