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CNEC2_0_extended_xlm-roberta-large

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xlm-roberta
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  1. README.md +24 -27
  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: 0.8492292870905588
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  - name: Recall
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  type: recall
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- value: 0.8749379652605459
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  - name: F1
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  type: f1
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- value: 0.8618919579564899
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  - name: Accuracy
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  type: accuracy
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- value: 0.973155737704918
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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: 0.1467
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- - Precision: 0.8492
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- - Recall: 0.8749
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- - F1: 0.8619
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- - Accuracy: 0.9732
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  ## Model description
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@@ -73,29 +73,26 @@ 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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- | 0.508 | 0.56 | 500 | 0.2177 | 0.6604 | 0.6928 | 0.6762 | 0.9423 |
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- | 0.2268 | 1.12 | 1000 | 0.1923 | 0.7158 | 0.7960 | 0.7538 | 0.9512 |
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- | 0.183 | 1.68 | 1500 | 0.1580 | 0.7825 | 0.8303 | 0.8057 | 0.9636 |
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- | 0.1558 | 2.24 | 2000 | 0.1548 | 0.8077 | 0.8382 | 0.8227 | 0.9676 |
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- | 0.1371 | 2.8 | 2500 | 0.1278 | 0.8233 | 0.8511 | 0.8370 | 0.9701 |
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- | 0.1225 | 3.36 | 3000 | 0.1430 | 0.8128 | 0.8531 | 0.8324 | 0.9667 |
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- | 0.1166 | 3.92 | 3500 | 0.1389 | 0.8307 | 0.8501 | 0.8403 | 0.9681 |
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- | 0.101 | 4.48 | 4000 | 0.1323 | 0.8277 | 0.8655 | 0.8462 | 0.9708 |
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- | 0.0928 | 5.04 | 4500 | 0.1332 | 0.8434 | 0.8660 | 0.8546 | 0.9715 |
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- | 0.0848 | 5.6 | 5000 | 0.1273 | 0.8382 | 0.8665 | 0.8521 | 0.9727 |
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- | 0.0798 | 6.16 | 5500 | 0.1281 | 0.8447 | 0.8774 | 0.8608 | 0.9716 |
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- | 0.0688 | 6.72 | 6000 | 0.1340 | 0.8482 | 0.8734 | 0.8606 | 0.9728 |
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- | 0.0638 | 7.28 | 6500 | 0.1346 | 0.8549 | 0.8744 | 0.8646 | 0.9746 |
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- | 0.0585 | 7.84 | 7000 | 0.1415 | 0.8442 | 0.8764 | 0.8600 | 0.9730 |
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- | 0.0565 | 8.4 | 7500 | 0.1487 | 0.8377 | 0.8809 | 0.8587 | 0.9730 |
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- | 0.0497 | 8.96 | 8000 | 0.1416 | 0.8473 | 0.8784 | 0.8626 | 0.9740 |
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- | 0.0484 | 9.52 | 8500 | 0.1467 | 0.8492 | 0.8749 | 0.8619 | 0.9732 |
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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: 0.8675623800383877
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  - name: Recall
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  type: recall
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+ value: 0.8972704714640198
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  - name: F1
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  type: f1
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+ value: 0.8821663820444011
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9754391100702576
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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: 0.1456
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+ - Precision: 0.8676
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+ - Recall: 0.8973
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+ - F1: 0.8822
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+ - Accuracy: 0.9754
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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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+ | 0.3439 | 0.56 | 500 | 0.1575 | 0.7882 | 0.8015 | 0.7948 | 0.9605 |
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+ | 0.1636 | 1.12 | 1000 | 0.1242 | 0.8071 | 0.8432 | 0.8248 | 0.9699 |
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+ | 0.1347 | 1.68 | 1500 | 0.1246 | 0.8273 | 0.8486 | 0.8378 | 0.9688 |
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+ | 0.105 | 2.24 | 2000 | 0.1276 | 0.8428 | 0.8645 | 0.8535 | 0.9727 |
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+ | 0.0942 | 2.8 | 2500 | 0.1263 | 0.8412 | 0.8809 | 0.8606 | 0.9734 |
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+ | 0.0778 | 3.36 | 3000 | 0.1178 | 0.8550 | 0.8779 | 0.8663 | 0.9746 |
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+ | 0.0696 | 3.92 | 3500 | 0.1168 | 0.8491 | 0.8878 | 0.8680 | 0.9738 |
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+ | 0.0565 | 4.48 | 4000 | 0.1135 | 0.8377 | 0.8734 | 0.8552 | 0.9734 |
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+ | 0.0532 | 5.04 | 4500 | 0.1218 | 0.8673 | 0.8888 | 0.8779 | 0.9752 |
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+ | 0.0451 | 5.6 | 5000 | 0.1339 | 0.8613 | 0.8878 | 0.8744 | 0.9751 |
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+ | 0.0396 | 6.16 | 5500 | 0.1339 | 0.8595 | 0.8864 | 0.8727 | 0.9751 |
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+ | 0.0331 | 6.72 | 6000 | 0.1361 | 0.8617 | 0.8933 | 0.8772 | 0.9755 |
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+ | 0.0263 | 7.28 | 6500 | 0.1450 | 0.8720 | 0.8958 | 0.8837 | 0.9758 |
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+ | 0.0278 | 7.84 | 7000 | 0.1456 | 0.8676 | 0.8973 | 0.8822 | 0.9754 |
 
 
 
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  ### Framework versions
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