---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base_original_esp-kin-eng_train
  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-base_original_esp-kin-eng_train

This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0301
- Spearman Corr: 0.7160

## 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: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Spearman Corr |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|
| No log        | 1.63  | 200  | 0.0292          | 0.6299        |
| 0.0375        | 3.27  | 400  | 0.0290          | 0.6845        |
| 0.0212        | 4.9   | 600  | 0.0259          | 0.6877        |
| 0.0164        | 6.53  | 800  | 0.0287          | 0.7094        |
| 0.0127        | 8.16  | 1000 | 0.0285          | 0.7219        |
| 0.0127        | 9.8   | 1200 | 0.0318          | 0.7103        |
| 0.0102        | 11.43 | 1400 | 0.0299          | 0.7125        |
| 0.008         | 13.06 | 1600 | 0.0357          | 0.7183        |
| 0.0067        | 14.69 | 1800 | 0.0282          | 0.7207        |
| 0.0056        | 16.33 | 2000 | 0.0314          | 0.7154        |
| 0.0056        | 17.96 | 2200 | 0.0317          | 0.7145        |
| 0.0049        | 19.59 | 2400 | 0.0302          | 0.7111        |
| 0.0044        | 21.22 | 2600 | 0.0301          | 0.7160        |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2