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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 +18 -17
  2. model.safetensors +1 -1
  3. runs/Mar07_20-42-34_g01/events.out.tfevents.1709840555.g01.769784.7 +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: 0.8325581395348837
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  - name: Recall
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  type: recall
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- value: 0.8824979457682827
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  - name: F1
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  type: f1
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- value: 0.8568009573195053
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  - name: Accuracy
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  type: accuracy
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- value: 0.965938712854081
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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: 0.1992
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- - Precision: 0.8326
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- - Recall: 0.8825
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- - F1: 0.8568
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- - Accuracy: 0.9659
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  ## Model description
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@@ -67,7 +67,7 @@ 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
@@ -75,18 +75,19 @@ The following hyperparameters were used during training:
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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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- | 0.5321 | 2.22 | 500 | 0.1641 | 0.7159 | 0.8065 | 0.7585 | 0.9566 |
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- | 0.1512 | 4.44 | 1000 | 0.1831 | 0.7886 | 0.8611 | 0.8233 | 0.9591 |
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- | 0.0967 | 6.67 | 1500 | 0.1866 | 0.7628 | 0.8628 | 0.8097 | 0.9596 |
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- | 0.0637 | 8.89 | 2000 | 0.1586 | 0.8054 | 0.8841 | 0.8429 | 0.9648 |
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- | 0.0422 | 11.11 | 2500 | 0.1777 | 0.8294 | 0.8648 | 0.8467 | 0.9654 |
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- | 0.0292 | 13.33 | 3000 | 0.1992 | 0.8326 | 0.8825 | 0.8568 | 0.9659 |
 
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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: 0.8282633808240277
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  - name: Recall
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  type: recall
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+ value: 0.8837304847986853
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  - name: F1
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  type: f1
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+ value: 0.8550983899821109
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9664021317268146
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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: 0.1865
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+ - Precision: 0.8283
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+ - Recall: 0.8837
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+ - F1: 0.8551
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+ - Accuracy: 0.9664
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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: 32
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  - eval_batch_size: 32
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  - seed: 42
 
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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: 16
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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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+ | 0.6852 | 2.22 | 500 | 0.1614 | 0.7278 | 0.8250 | 0.7733 | 0.9574 |
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+ | 0.1311 | 4.44 | 1000 | 0.1716 | 0.7690 | 0.8591 | 0.8116 | 0.9596 |
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+ | 0.0882 | 6.67 | 1500 | 0.1785 | 0.7616 | 0.8714 | 0.8128 | 0.9613 |
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+ | 0.062 | 8.89 | 2000 | 0.1536 | 0.8212 | 0.8928 | 0.8555 | 0.9669 |
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+ | 0.0457 | 11.11 | 2500 | 0.1783 | 0.8204 | 0.8673 | 0.8432 | 0.9645 |
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+ | 0.0353 | 13.33 | 3000 | 0.1829 | 0.8259 | 0.8809 | 0.8525 | 0.9655 |
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+ | 0.0289 | 15.56 | 3500 | 0.1865 | 0.8283 | 0.8837 | 0.8551 | 0.9664 |
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  ### Framework versions
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