---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: metadata-cls-no-gov-8k-vnnic-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. -->

# metadata-cls-no-gov-8k-vnnic-xml

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: 0.3308
- Accuracy: 0.9345
- F1: 0.7072

## 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: 32
- eval_batch_size: 32
- 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 | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
| No log        | 0.5703 | 150  | 0.2151          | 0.9411   | 0.4945 |
| 0.5567        | 1.1407 | 300  | 0.2789          | 0.9121   | 0.4579 |
| 0.5567        | 1.7110 | 450  | 0.2676          | 0.9130   | 0.6930 |
| 0.2703        | 2.2814 | 600  | 0.1926          | 0.9345   | 0.7071 |
| 0.2703        | 2.8517 | 750  | 0.1984          | 0.9411   | 0.7390 |
| 0.2055        | 3.4221 | 900  | 0.2503          | 0.9205   | 0.6689 |
| 0.1573        | 3.9924 | 1050 | 0.1895          | 0.9476   | 0.7332 |
| 0.1573        | 4.5627 | 1200 | 0.3001          | 0.9130   | 0.6974 |
| 0.1287        | 5.1331 | 1350 | 0.1834          | 0.9495   | 0.7302 |
| 0.1287        | 5.7034 | 1500 | 0.2586          | 0.9429   | 0.7345 |
| 0.1022        | 6.2738 | 1650 | 0.3486          | 0.9261   | 0.7055 |
| 0.1022        | 6.8441 | 1800 | 0.3064          | 0.9317   | 0.7053 |
| 0.085         | 7.4144 | 1950 | 0.3445          | 0.9308   | 0.7086 |
| 0.0689        | 7.9848 | 2100 | 0.3342          | 0.9336   | 0.7130 |
| 0.0689        | 8.5551 | 2250 | 0.3272          | 0.9345   | 0.7074 |
| 0.0525        | 9.1255 | 2400 | 0.3391          | 0.9345   | 0.7030 |
| 0.0525        | 9.6958 | 2550 | 0.3308          | 0.9345   | 0.7072 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1