---
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlm-roberta-large_latest_Nov2023
  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. -->

# xlm-roberta-large_latest_Nov2023

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3474
- Accuracy: 0.7735

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6171        | 0.2   | 100  | 0.5548          | 0.569    |
| 0.5233        | 0.4   | 200  | 0.4284          | 0.715    |
| 0.4572        | 0.6   | 300  | 0.4136          | 0.7185   |
| 0.4347        | 0.8   | 400  | 0.4087          | 0.7065   |
| 0.4379        | 1.0   | 500  | 0.4107          | 0.7275   |
| 0.4285        | 1.2   | 600  | 0.4007          | 0.7285   |
| 0.3897        | 1.4   | 700  | 0.3986          | 0.7315   |
| 0.3862        | 1.6   | 800  | 0.3536          | 0.76     |
| 0.3575        | 1.8   | 900  | 0.3506          | 0.762    |
| 0.3247        | 2.0   | 1000 | 0.3474          | 0.7735   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1