---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: xlm-roberta-large-DreamBank
  results: []
widget:
- text: >-
    I dreamed that Hannah and Sue and I travelled back in time to meet her
    parents. Weird.
pipeline_tag: text-classification
---

<!-- 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-DreamBank

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
Best result (loaded model)
- F1: 0.8621

## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| No log        | 1.0   | 185  | 0.5949          | 0.0    | 0.5     | 0.0      |
| No log        | 2.0   | 370  | 0.3825          | 0.6052 | 0.7481  | 0.4595   |
| 0.476         | 3.0   | 555  | 0.2891          | 0.7403 | 0.8010  | 0.5730   |
| 0.476         | 4.0   | 740  | 0.2604          | 0.8425 | 0.8852  | 0.7081   |
| 0.476         | 5.0   | 925  | 0.2484          | 0.8504 | 0.8932  | 0.6649   |
| 0.1457        | 6.0   | 1110 | 0.3092          | 0.8352 | 0.8909  | 0.6703   |
| 0.1457        | 7.0   | 1295 | 0.2882          | 0.8546 | 0.8950  | 0.6919   |
| 0.1457        | 8.0   | 1480 | 0.3099          | 0.8549 | 0.9014  | 0.6865   |
| 0.0691        | 9.0   | 1665 | 0.3080          | 0.8548 | 0.9019  | 0.6811   |
| 0.0691        | 10.0  | 1850 | 0.2942          | 0.8621 | 0.9069  | 0.6973   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.12.1