---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: 16052025_xlm_roberta_large_test_linsearch_only_abstract
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# 16052025_xlm_roberta_large_test_linsearch_only_abstract

This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3318
- Accuracy: 0.5931
- F1 Macro: 0.5650
- Precision Macro: 0.5755
- Recall Macro: 0.5602

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use 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_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Accuracy | F1 Macro | Precision Macro | Recall Macro |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|:---------------:|:------------:|
| 1.3362        | 0.9999 | 7024  | 1.3026          | 0.5520   | 0.4904   | 0.5266          | 0.4959       |
| 1.2241        | 1.9999 | 14048 | 1.2036          | 0.5773   | 0.5348   | 0.5535          | 0.5395       |
| 1.1337        | 2.9999 | 21072 | 1.1903          | 0.5760   | 0.5303   | 0.5466          | 0.5278       |
| 1.069         | 3.9999 | 28096 | 1.1570          | 0.5876   | 0.5418   | 0.5564          | 0.5439       |
| 0.9832        | 4.9999 | 35120 | 1.1723          | 0.5872   | 0.5461   | 0.5570          | 0.5461       |
| 0.9197        | 5.9999 | 42144 | 1.1752          | 0.5871   | 0.5455   | 0.5456          | 0.5572       |
| 0.8278        | 6.9999 | 49168 | 1.2078          | 0.5928   | 0.5537   | 0.5616          | 0.5589       |
| 0.7568        | 7.9999 | 56192 | 1.2398          | 0.5923   | 0.5563   | 0.5612          | 0.5568       |
| 0.6863        | 8.9999 | 63216 | 1.2840          | 0.5937   | 0.5556   | 0.5723          | 0.5465       |
| 0.63          | 9.9999 | 70240 | 1.3318          | 0.5931   | 0.5650   | 0.5755          | 0.5602       |


### Framework versions

- Transformers 4.48.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0