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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 +25 -21
  2. runs/Mar07_17-20-24_g01/events.out.tfevents.1709828424.g01.769784.2 +2 -2
README.md CHANGED
@@ -20,21 +20,21 @@ model-index:
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  name: cnec
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  type: cnec
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  config: default
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- split: validation
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  args: default
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.8222823635543527
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  - name: Recall
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  type: recall
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- value: 0.8798262548262549
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  - name: F1
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  type: f1
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- value: 0.8500816041035206
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  - name: Accuracy
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  type: accuracy
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- value: 0.9681297986382732
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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: 0.2071
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- - Precision: 0.8223
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- - Recall: 0.8798
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- - F1: 0.8501
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- - Accuracy: 0.9681
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  ## Model description
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@@ -67,26 +67,30 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 5e-05
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- - train_batch_size: 32
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- - eval_batch_size: 32
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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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  - lr_scheduler_warmup_ratio: 0.1
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- - lr_scheduler_warmup_steps: 500
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- - num_epochs: 15
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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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- | 0.4978 | 2.22 | 500 | 0.1737 | 0.6882 | 0.8118 | 0.7449 | 0.9548 |
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- | 0.149 | 4.44 | 1000 | 0.1573 | 0.7540 | 0.8552 | 0.8014 | 0.9596 |
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- | 0.0796 | 6.67 | 1500 | 0.1530 | 0.8024 | 0.8760 | 0.8376 | 0.9648 |
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- | 0.0473 | 8.89 | 2000 | 0.1539 | 0.8051 | 0.8731 | 0.8377 | 0.9675 |
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- | 0.0272 | 11.11 | 2500 | 0.2028 | 0.7973 | 0.8581 | 0.8266 | 0.9643 |
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- | 0.0154 | 13.33 | 3000 | 0.2071 | 0.8223 | 0.8798 | 0.8501 | 0.9681 |
 
 
 
 
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  ### Framework versions
 
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  name: cnec
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  type: cnec
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  config: default
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+ split: test
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  args: default
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.8236658932714617
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  - name: Recall
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  type: recall
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+ value: 0.8751027115858668
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  - name: F1
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  type: f1
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+ value: 0.848605577689243
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9646932746336094
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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: 0.2014
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+ - Precision: 0.8237
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+ - Recall: 0.8751
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+ - F1: 0.8486
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+ - Accuracy: 0.9647
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  ## Model description
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  ### Training hyperparameters
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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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  - lr_scheduler_warmup_ratio: 0.1
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+ - lr_scheduler_warmup_steps: 1000
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+ - num_epochs: 12
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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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+ | 0.9082 | 1.11 | 500 | 0.2281 | 0.6024 | 0.7539 | 0.6697 | 0.9424 |
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+ | 0.1977 | 2.22 | 1000 | 0.1808 | 0.7211 | 0.8369 | 0.7747 | 0.9544 |
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+ | 0.1477 | 3.33 | 1500 | 0.1674 | 0.7716 | 0.8661 | 0.8161 | 0.9612 |
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+ | 0.1105 | 4.44 | 2000 | 0.1628 | 0.7860 | 0.8780 | 0.8294 | 0.9633 |
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+ | 0.0929 | 5.56 | 2500 | 0.1609 | 0.7982 | 0.8743 | 0.8345 | 0.9629 |
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+ | 0.0735 | 6.67 | 3000 | 0.1740 | 0.7901 | 0.8722 | 0.8291 | 0.9625 |
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+ | 0.0614 | 7.78 | 3500 | 0.1860 | 0.8027 | 0.8710 | 0.8355 | 0.9641 |
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+ | 0.0513 | 8.89 | 4000 | 0.1823 | 0.8038 | 0.8804 | 0.8404 | 0.9633 |
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+ | 0.0399 | 10.0 | 4500 | 0.1866 | 0.8103 | 0.8846 | 0.8458 | 0.9639 |
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+ | 0.0327 | 11.11 | 5000 | 0.2014 | 0.8237 | 0.8751 | 0.8486 | 0.9647 |
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  ### Framework versions
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