---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: test_linsearch_only_abstract
  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. -->

# test_linsearch_only_abstract

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: 1.1038
- Accuracy: 0.6548
- Precision: 0.6548
- Recall: 0.6548
- F1: 0.6548

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.19          | 1.0   | 5122  | 1.1851          | 0.6284   | 0.6284    | 0.6284 | 0.6284 |
| 1.0146        | 2.0   | 10244 | 1.1038          | 0.6548   | 0.6548    | 0.6548 | 0.6548 |
| 0.8889        | 3.0   | 15366 | 1.1175          | 0.6511   | 0.6511    | 0.6511 | 0.6511 |
| 0.7382        | 4.0   | 20488 | 1.1474          | 0.6537   | 0.6537    | 0.6537 | 0.6537 |
| 0.6511        | 5.0   | 25610 | 1.1825          | 0.6512   | 0.6512    | 0.6512 | 0.6512 |


### Framework versions

- Transformers 4.50.1
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1