---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base_kin_corr_2e-05
  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_kin_corr_2e-05

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: 0.0213
- Spearman Corr: 0.7790

## 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        | 0.89  | 200  | 0.0210          | 0.7800        |
| No log        | 1.78  | 400  | 0.0210          | 0.7820        |
| 0.0006        | 2.67  | 600  | 0.0208          | 0.7800        |
| 0.0006        | 3.56  | 800  | 0.0209          | 0.7822        |
| 0.0006        | 4.45  | 1000 | 0.0212          | 0.7801        |
| 0.0006        | 5.35  | 1200 | 0.0214          | 0.7792        |
| 0.0005        | 6.24  | 1400 | 0.0213          | 0.7768        |
| 0.0005        | 7.13  | 1600 | 0.0211          | 0.7803        |
| 0.0005        | 8.02  | 1800 | 0.0210          | 0.7785        |
| 0.0005        | 8.91  | 2000 | 0.0213          | 0.7783        |
| 0.0005        | 9.8   | 2200 | 0.0211          | 0.7809        |
| 0.0005        | 10.69 | 2400 | 0.0213          | 0.7790        |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2