---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: fm-tc-end_mix_xml
  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. -->

# fm-tc-end_mix_xml

This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2061
- Accuracy: 0.97
- Precision: 0.9706
- Recall: 0.9700
- F1: 0.9700

## 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: 5e-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: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6642        | 1.0   | 1188  | 0.4540          | 0.894    | 0.8983    | 0.8940 | 0.8938 |
| 0.4074        | 2.0   | 2376  | 0.4206          | 0.918    | 0.9241    | 0.9180 | 0.9183 |
| 0.2403        | 3.0   | 3564  | 0.4380          | 0.918    | 0.9221    | 0.9180 | 0.9165 |
| 0.1625        | 4.0   | 4752  | 0.4773          | 0.926    | 0.9297    | 0.9260 | 0.9262 |
| 0.0991        | 5.0   | 5940  | 0.2999          | 0.952    | 0.9536    | 0.9520 | 0.9521 |
| 0.0549        | 6.0   | 7128  | 0.2217          | 0.966    | 0.9671    | 0.966  | 0.9659 |
| 0.0226        | 7.0   | 8316  | 0.2770          | 0.964    | 0.9650    | 0.9640 | 0.9637 |
| 0.0154        | 8.0   | 9504  | 0.2061          | 0.97     | 0.9706    | 0.9700 | 0.9700 |
| 0.0061        | 9.0   | 10692 | 0.2372          | 0.97     | 0.9706    | 0.9700 | 0.9699 |
| 0.0048        | 10.0  | 11880 | 0.2419          | 0.964    | 0.9650    | 0.9640 | 0.9640 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1