---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-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. -->

# xlm-roberta-base-finetuned

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the 'Contradictory, My Dear Watson' kaggle competition dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7526
- Accuracy: 0.7075
- F1: 0.7080

## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 76   | 1.0440          | 0.5004   | 0.5007 |
| 1.03          | 2.0   | 152  | 0.8601          | 0.6370   | 0.6374 |
| 1.03          | 3.0   | 228  | 0.7761          | 0.6712   | 0.6727 |
| 0.7667        | 4.0   | 304  | 0.7526          | 0.6951   | 0.6967 |
| 0.7667        | 5.0   | 380  | 0.7526          | 0.7075   | 0.7080 |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0