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---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NeRUBioS_xlm_RoBERTa_base_Training_Testing
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# NeRUBioS_xlm_RoBERTa_base_Training_Testing
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3585
- Negref Precision: 0.5638
- Negref Recall: 0.6035
- Negref F1: 0.5830
- Neg Precision: 0.9508
- Neg Recall: 0.9642
- Neg F1: 0.9575
- Nsco Precision: 0.8692
- Nsco Recall: 0.9047
- Nsco F1: 0.8866
- Unc Precision: 0.8005
- Unc Recall: 0.8846
- Unc F1: 0.8404
- Usco Precision: 0.6696
- Usco Recall: 0.7815
- Usco F1: 0.7212
- Precision: 0.8184
- Recall: 0.8628
- F1: 0.8400
- Accuracy: 0.9482
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Negref Precision | Negref Recall | Negref F1 | Neg Precision | Neg Recall | Neg F1 | Nsco Precision | Nsco Recall | Nsco F1 | Unc Precision | Unc Recall | Unc F1 | Usco Precision | Usco Recall | Usco F1 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|:---------:|:------:|:------:|:--------:|
| 0.2259 | 1.0 | 1729 | 0.2246 | 0.4076 | 0.4890 | 0.4446 | 0.9112 | 0.9515 | 0.9310 | 0.7928 | 0.8654 | 0.8275 | 0.7015 | 0.8256 | 0.7585 | 0.4629 | 0.6735 | 0.5487 | 0.7158 | 0.8122 | 0.7610 | 0.9287 |
| 0.16 | 2.0 | 3458 | 0.2028 | 0.5217 | 0.5301 | 0.5259 | 0.9283 | 0.9642 | 0.9459 | 0.8311 | 0.8896 | 0.8593 | 0.7812 | 0.8513 | 0.8147 | 0.5734 | 0.7532 | 0.6511 | 0.7817 | 0.8405 | 0.8100 | 0.9397 |
| 0.1235 | 3.0 | 5187 | 0.2148 | 0.5176 | 0.4963 | 0.5067 | 0.9520 | 0.9607 | 0.9563 | 0.8641 | 0.8850 | 0.8744 | 0.7684 | 0.8846 | 0.8224 | 0.6113 | 0.7481 | 0.6728 | 0.8038 | 0.8350 | 0.8191 | 0.9439 |
| 0.0949 | 4.0 | 6916 | 0.2261 | 0.5054 | 0.6211 | 0.5573 | 0.9327 | 0.9642 | 0.9482 | 0.8450 | 0.8828 | 0.8635 | 0.7976 | 0.8487 | 0.8224 | 0.6034 | 0.7352 | 0.6628 | 0.7818 | 0.8512 | 0.8150 | 0.9450 |
| 0.0633 | 5.0 | 8645 | 0.2354 | 0.5609 | 0.5947 | 0.5773 | 0.9417 | 0.9649 | 0.9532 | 0.8669 | 0.9062 | 0.8861 | 0.8062 | 0.8641 | 0.8342 | 0.6334 | 0.7506 | 0.6871 | 0.8118 | 0.8573 | 0.8340 | 0.9461 |
| 0.0495 | 6.0 | 10374 | 0.2829 | 0.5585 | 0.5962 | 0.5767 | 0.9445 | 0.9684 | 0.9563 | 0.8671 | 0.9077 | 0.8869 | 0.8116 | 0.8615 | 0.8358 | 0.6526 | 0.7532 | 0.6993 | 0.8151 | 0.8592 | 0.8366 | 0.9442 |
| 0.0365 | 7.0 | 12103 | 0.2699 | 0.5446 | 0.5830 | 0.5631 | 0.9552 | 0.9572 | 0.9562 | 0.8804 | 0.9024 | 0.8913 | 0.8080 | 0.8846 | 0.8446 | 0.6521 | 0.7661 | 0.7045 | 0.8182 | 0.8550 | 0.8362 | 0.9473 |
| 0.0265 | 8.0 | 13832 | 0.3082 | 0.5630 | 0.5580 | 0.5605 | 0.9466 | 0.9593 | 0.9529 | 0.8702 | 0.9024 | 0.8860 | 0.8038 | 0.8718 | 0.8364 | 0.6571 | 0.7635 | 0.7063 | 0.8194 | 0.8502 | 0.8345 | 0.9460 |
| 0.0216 | 9.0 | 15561 | 0.3286 | 0.5485 | 0.5977 | 0.5720 | 0.9388 | 0.9691 | 0.9537 | 0.8715 | 0.9077 | 0.8892 | 0.8085 | 0.8769 | 0.8413 | 0.6453 | 0.7763 | 0.7048 | 0.8105 | 0.8633 | 0.8361 | 0.9455 |
| 0.0133 | 10.0 | 17290 | 0.3503 | 0.5732 | 0.6094 | 0.5907 | 0.9481 | 0.9628 | 0.9554 | 0.8698 | 0.8994 | 0.8843 | 0.8137 | 0.8846 | 0.8477 | 0.6816 | 0.7815 | 0.7281 | 0.8223 | 0.8616 | 0.8415 | 0.9482 |
| 0.0088 | 11.0 | 19019 | 0.3476 | 0.5584 | 0.6182 | 0.5868 | 0.9450 | 0.9656 | 0.9552 | 0.8614 | 0.9070 | 0.8836 | 0.8080 | 0.8846 | 0.8446 | 0.6659 | 0.7789 | 0.7180 | 0.8126 | 0.8661 | 0.8385 | 0.9483 |
| 0.0093 | 12.0 | 20748 | 0.3585 | 0.5638 | 0.6035 | 0.5830 | 0.9508 | 0.9642 | 0.9575 | 0.8692 | 0.9047 | 0.8866 | 0.8005 | 0.8846 | 0.8404 | 0.6696 | 0.7815 | 0.7212 | 0.8184 | 0.8628 | 0.8400 | 0.9482 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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