Fine-tuning completed
Browse files
README.md
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---
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license: mit
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base_model: FacebookAI/xlm-roberta-base
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tags:
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- generated_from_trainer
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: RE_NegREF_NSD_Nubes_Training_Development_dataset_xlm_RoBERTa_base_fine_tuned
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results: []
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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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should probably proofread and complete it, then remove this comment. -->
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# RE_NegREF_NSD_Nubes_Training_Development_dataset_xlm_RoBERTa_base_fine_tuned
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This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2774
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- Negref Precision: 0.5671
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- Negref Recall: 0.5899
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- Negref F1: 0.5782
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- Neg Precision: 0.9511
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- Neg Recall: 0.9771
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- Neg F1: 0.9639
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- Nsco Precision: 0.8734
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- Nsco Recall: 0.9181
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- Nsco F1: 0.8952
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- Precision: 0.8399
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- Recall: 0.8727
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- F1: 0.8560
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- Accuracy: 0.9615
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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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: 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: 12
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Negref Precision | Negref Recall | Negref F1 | Neg Precision | Neg Recall | Neg F1 | Nsco Precision | Nsco Recall | Nsco F1 | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|:---------:|:------:|:------:|:--------:|
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| 0.1739 | 1.0 | 1729 | 0.1433 | 0.4553 | 0.4419 | 0.4485 | 0.9216 | 0.9629 | 0.9418 | 0.7944 | 0.8717 | 0.8313 | 0.7799 | 0.8180 | 0.7985 | 0.9555 |
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| 0.1189 | 2.0 | 3458 | 0.1388 | 0.4859 | 0.5476 | 0.5149 | 0.9230 | 0.9683 | 0.9451 | 0.7951 | 0.8895 | 0.8397 | 0.7779 | 0.8494 | 0.8121 | 0.9569 |
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| 0.0944 | 3.0 | 5187 | 0.1374 | 0.5771 | 0.5539 | 0.5653 | 0.9495 | 0.9640 | 0.9567 | 0.8535 | 0.8860 | 0.8695 | 0.8375 | 0.8476 | 0.8425 | 0.9615 |
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| 0.0703 | 4.0 | 6916 | 0.1524 | 0.5415 | 0.6068 | 0.5723 | 0.9410 | 0.9749 | 0.9576 | 0.8476 | 0.8848 | 0.8658 | 0.8164 | 0.8628 | 0.8390 | 0.9602 |
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| 0.0472 | 5.0 | 8645 | 0.1812 | 0.5306 | 0.5687 | 0.5490 | 0.9368 | 0.9716 | 0.9539 | 0.8440 | 0.8931 | 0.8679 | 0.8139 | 0.8566 | 0.8347 | 0.9585 |
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| 0.0371 | 6.0 | 10374 | 0.2231 | 0.5371 | 0.5814 | 0.5584 | 0.9286 | 0.9803 | 0.9538 | 0.8448 | 0.9181 | 0.8799 | 0.8129 | 0.8723 | 0.8415 | 0.9588 |
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| 0.031 | 7.0 | 12103 | 0.2036 | 0.5362 | 0.5793 | 0.5569 | 0.9509 | 0.9716 | 0.9611 | 0.8830 | 0.9145 | 0.8985 | 0.8340 | 0.8669 | 0.8501 | 0.9617 |
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| 0.0226 | 8.0 | 13832 | 0.2390 | 0.5392 | 0.5666 | 0.5526 | 0.9507 | 0.9694 | 0.9600 | 0.8751 | 0.9157 | 0.8950 | 0.8335 | 0.8637 | 0.8483 | 0.9589 |
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| 0.0174 | 9.0 | 15561 | 0.2462 | 0.5355 | 0.5899 | 0.5614 | 0.9443 | 0.9803 | 0.9620 | 0.8694 | 0.9169 | 0.8925 | 0.8258 | 0.8736 | 0.8491 | 0.9599 |
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| 0.0098 | 10.0 | 17290 | 0.2751 | 0.5796 | 0.5772 | 0.5784 | 0.9490 | 0.9749 | 0.9618 | 0.8740 | 0.9145 | 0.8938 | 0.8443 | 0.8678 | 0.8559 | 0.9621 |
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| 0.008 | 11.0 | 19019 | 0.2760 | 0.5611 | 0.5729 | 0.5669 | 0.9461 | 0.9771 | 0.9613 | 0.8695 | 0.9181 | 0.8931 | 0.8365 | 0.8691 | 0.8525 | 0.9606 |
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| 0.0041 | 12.0 | 20748 | 0.2774 | 0.5671 | 0.5899 | 0.5782 | 0.9511 | 0.9771 | 0.9639 | 0.8734 | 0.9181 | 0.8952 | 0.8399 | 0.8727 | 0.8560 | 0.9615 |
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### Framework versions
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- Transformers 4.38.2
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- Pytorch 2.2.1+cu121
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- Datasets 2.18.0
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- Tokenizers 0.15.2
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