---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: xlmr-finetuned
  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. -->

# xlmr-finetuned

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3897

## 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: 16
- 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.1718        | 0.29  | 500   | 2.5733          |
| 2.8822        | 0.59  | 1000  | 2.3739          |
| 2.7361        | 0.88  | 1500  | 2.3563          |
| 2.6077        | 1.18  | 2000  | 2.2466          |
| 2.4731        | 1.47  | 2500  | 2.2027          |
| 2.4545        | 1.76  | 3000  | 2.2104          |
| 2.467         | 2.06  | 3500  | 2.0885          |
| 2.3209        | 2.35  | 4000  | 2.0476          |
| 2.2937        | 2.64  | 4500  | 1.9431          |
| 2.2624        | 2.94  | 5000  | 1.9157          |
| 2.1502        | 3.23  | 5500  | 1.8811          |
| 2.1445        | 3.53  | 6000  | 1.8488          |
| 2.1308        | 3.82  | 6500  | 1.8074          |
| 2.0752        | 4.11  | 7000  | 1.8089          |
| 2.032         | 4.41  | 7500  | 1.7853          |
| 2.0253        | 4.7   | 8000  | 1.7723          |
| 1.9904        | 4.99  | 8500  | 1.6976          |
| 1.9348        | 5.29  | 9000  | 1.6399          |
| 1.9116        | 5.58  | 9500  | 1.6159          |
| 1.9105        | 5.88  | 10000 | 1.5930          |
| 1.8649        | 6.17  | 10500 | 1.5590          |
| 1.8108        | 6.46  | 11000 | 1.5662          |
| 1.8084        | 6.76  | 11500 | 1.5504          |
| 1.7835        | 7.05  | 12000 | 1.5933          |
| 1.7324        | 7.34  | 12500 | 1.5500          |
| 1.7358        | 7.64  | 13000 | 1.4570          |
| 1.726         | 7.93  | 13500 | 1.4775          |
| 1.6477        | 8.23  | 14000 | 1.4382          |
| 1.6768        | 8.52  | 14500 | 1.4717          |
| 1.6073        | 8.81  | 15000 | 1.4162          |
| 1.6516        | 9.11  | 15500 | 1.4516          |
| 1.6084        | 9.4   | 16000 | 1.4209          |
| 1.6013        | 9.69  | 16500 | 1.3874          |
| 1.608         | 9.99  | 17000 | 1.3897          |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.0.0
- Datasets 2.16.1
- Tokenizers 0.15.0