---
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: fine_tuned_XLMROBERTA_cs_wikann
  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. -->

# fine_tuned_XLMROBERTA_cs_wikann

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: 0.1699
- Precision: 0.9133
- Recall: 0.9319
- F1: 0.9225
- Accuracy: 0.9699

## 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: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7699        | 0.2   | 500   | 0.3588          | 0.5878    | 0.6990 | 0.6386 | 0.8894   |
| 0.3658        | 0.4   | 1000  | 0.2538          | 0.7427    | 0.8258 | 0.7821 | 0.9355   |
| 0.301         | 0.6   | 1500  | 0.2403          | 0.7649    | 0.8237 | 0.7932 | 0.9400   |
| 0.2796        | 0.8   | 2000  | 0.1828          | 0.7967    | 0.8509 | 0.8229 | 0.9456   |
| 0.258         | 1.0   | 2500  | 0.2223          | 0.7770    | 0.8322 | 0.8037 | 0.9400   |
| 0.2192        | 1.2   | 3000  | 0.1911          | 0.8156    | 0.8745 | 0.8440 | 0.9511   |
| 0.2161        | 1.4   | 3500  | 0.1878          | 0.8401    | 0.8858 | 0.8623 | 0.9551   |
| 0.2095        | 1.6   | 4000  | 0.1916          | 0.8306    | 0.8783 | 0.8538 | 0.9559   |
| 0.2137        | 1.8   | 4500  | 0.1657          | 0.8573    | 0.8874 | 0.8721 | 0.9585   |
| 0.1884        | 2.0   | 5000  | 0.2134          | 0.8486    | 0.8837 | 0.8658 | 0.9542   |
| 0.164         | 2.2   | 5500  | 0.2038          | 0.8619    | 0.9048 | 0.8828 | 0.9588   |
| 0.1564        | 2.4   | 6000  | 0.1707          | 0.8502    | 0.8874 | 0.8684 | 0.9582   |
| 0.1719        | 2.6   | 6500  | 0.1781          | 0.8645    | 0.8994 | 0.8816 | 0.9610   |
| 0.1565        | 2.8   | 7000  | 0.1908          | 0.8712    | 0.9021 | 0.8864 | 0.9614   |
| 0.1713        | 3.0   | 7500  | 0.1628          | 0.8672    | 0.8954 | 0.8811 | 0.9623   |
| 0.1359        | 3.2   | 8000  | 0.1890          | 0.8684    | 0.9072 | 0.8874 | 0.9624   |
| 0.1362        | 3.4   | 8500  | 0.1672          | 0.8653    | 0.9065 | 0.8854 | 0.9620   |
| 0.1301        | 3.6   | 9000  | 0.1866          | 0.8698    | 0.9069 | 0.8879 | 0.9631   |
| 0.1345        | 3.8   | 9500  | 0.1766          | 0.8759    | 0.9071 | 0.8913 | 0.9647   |
| 0.1363        | 4.0   | 10000 | 0.1817          | 0.8700    | 0.9137 | 0.8913 | 0.9626   |
| 0.1097        | 4.2   | 10500 | 0.1611          | 0.8861    | 0.9118 | 0.8987 | 0.9653   |
| 0.1045        | 4.4   | 11000 | 0.1743          | 0.8899    | 0.9123 | 0.9009 | 0.9659   |
| 0.1068        | 4.6   | 11500 | 0.1771          | 0.8870    | 0.9167 | 0.9016 | 0.9660   |
| 0.1168        | 4.8   | 12000 | 0.1704          | 0.8894    | 0.9174 | 0.9032 | 0.9660   |
| 0.1116        | 5.0   | 12500 | 0.1748          | 0.8926    | 0.9203 | 0.9062 | 0.9673   |
| 0.0979        | 5.2   | 13000 | 0.1726          | 0.8956    | 0.9255 | 0.9103 | 0.9672   |
| 0.0992        | 5.4   | 13500 | 0.1798          | 0.9058    | 0.9280 | 0.9168 | 0.9686   |
| 0.0929        | 5.6   | 14000 | 0.1740          | 0.9063    | 0.9304 | 0.9182 | 0.9693   |
| 0.098         | 5.8   | 14500 | 0.1690          | 0.8931    | 0.9262 | 0.9094 | 0.9683   |
| 0.0878        | 6.0   | 15000 | 0.1682          | 0.9065    | 0.9294 | 0.9178 | 0.9696   |
| 0.0925        | 6.2   | 15500 | 0.1691          | 0.9102    | 0.9308 | 0.9204 | 0.9694   |
| 0.0841        | 6.4   | 16000 | 0.1657          | 0.9138    | 0.9298 | 0.9217 | 0.9699   |
| 0.0748        | 6.6   | 16500 | 0.1696          | 0.9114    | 0.9313 | 0.9213 | 0.9695   |
| 0.0753        | 6.8   | 17000 | 0.1703          | 0.9118    | 0.9311 | 0.9214 | 0.9697   |
| 0.073         | 7.0   | 17500 | 0.1699          | 0.9133    | 0.9319 | 0.9225 | 0.9699   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0