---
license: mit
base_model: nielsr/lilt-xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: test
  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

This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co./nielsr/lilt-xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6516
- Precision: 0.7245
- Recall: 0.7621
- F1: 0.7428
- Accuracy: 0.7700

## 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.33  | 100  | 0.9064          | 0.4989    | 0.6694 | 0.5717 | 0.6558   |
| No log        | 2.67  | 200  | 0.9830          | 0.5946    | 0.5986 | 0.5966 | 0.6988   |
| No log        | 4.0   | 300  | 0.8347          | 0.6432    | 0.6943 | 0.6678 | 0.7418   |
| No log        | 5.33  | 400  | 0.8003          | 0.6759    | 0.7341 | 0.7038 | 0.7710   |
| 0.6429        | 6.67  | 500  | 0.9784          | 0.6887    | 0.7336 | 0.7104 | 0.7645   |
| 0.6429        | 8.0   | 600  | 0.9918          | 0.7099    | 0.7529 | 0.7308 | 0.7565   |
| 0.6429        | 9.33  | 700  | 1.1164          | 0.7102    | 0.7264 | 0.7182 | 0.7528   |
| 0.6429        | 10.67 | 800  | 1.3786          | 0.6997    | 0.7621 | 0.7296 | 0.7429   |
| 0.6429        | 12.0  | 900  | 1.2818          | 0.7168    | 0.7529 | 0.7344 | 0.7617   |
| 0.106         | 13.33 | 1000 | 1.3933          | 0.7004    | 0.7407 | 0.7200 | 0.7465   |
| 0.106         | 14.67 | 1100 | 1.3226          | 0.7000    | 0.7641 | 0.7306 | 0.7653   |
| 0.106         | 16.0  | 1200 | 1.5013          | 0.7166    | 0.7509 | 0.7333 | 0.7508   |
| 0.106         | 17.33 | 1300 | 1.4213          | 0.7165    | 0.7427 | 0.7294 | 0.7732   |
| 0.106         | 18.67 | 1400 | 1.4495          | 0.7144    | 0.7366 | 0.7254 | 0.7722   |
| 0.0248        | 20.0  | 1500 | 1.5319          | 0.7226    | 0.7326 | 0.7275 | 0.7717   |
| 0.0248        | 21.33 | 1600 | 1.5563          | 0.7232    | 0.7626 | 0.7424 | 0.7731   |
| 0.0248        | 22.67 | 1700 | 1.5967          | 0.7364    | 0.7657 | 0.7507 | 0.7734   |
| 0.0248        | 24.0  | 1800 | 1.5916          | 0.7375    | 0.7616 | 0.7494 | 0.7773   |
| 0.0248        | 25.33 | 1900 | 1.6402          | 0.7267    | 0.7504 | 0.7383 | 0.7719   |
| 0.0069        | 26.67 | 2000 | 1.6516          | 0.7250    | 0.7575 | 0.7409 | 0.7688   |
| 0.0069        | 28.0  | 2100 | 1.6539          | 0.7262    | 0.7621 | 0.7437 | 0.7697   |
| 0.0069        | 29.33 | 2200 | 1.6516          | 0.7245    | 0.7621 | 0.7428 | 0.7700   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1