transformers pytorch xlm-roberta generated_from_trainer dataset:massive base_model:xlm-roberta-base license:mit endpoints_compatible region:us
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- massive
metrics:
- accuracy
- f1
model-index:
- name: scenario-NON-KD-PR-COPY-D2_data-cl-massive_all_1_1
  results: []
---

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# scenario-NON-KD-PR-COPY-D2_data-cl-massive_all_1_1

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5572
- Accuracy: 0.7104
- F1: 0.6571

## 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: 32
- eval_batch_size: 32
- seed: 47
- 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.6773        | 0.56  | 5000  | 1.2533          | 0.6798   | 0.6034 |
| 0.4068        | 1.11  | 10000 | 1.2635          | 0.6928   | 0.6377 |
| 0.348         | 1.67  | 15000 | 1.2363          | 0.7094   | 0.6485 |
| 0.2334        | 2.22  | 20000 | 1.3851          | 0.6993   | 0.6422 |
| 0.2174        | 2.78  | 25000 | 1.3811          | 0.7042   | 0.6456 |
| 0.1405        | 3.33  | 30000 | 1.4452          | 0.7050   | 0.6499 |
| 0.1396        | 3.89  | 35000 | 1.4728          | 0.7116   | 0.6564 |
| 0.0957        | 4.45  | 40000 | 1.5572          | 0.7104   | 0.6571 |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
