transformers pytorch xlm-roberta generated_from_trainer dataset:massive base_model:FacebookAI/xlm-roberta-base license:mit endpoints_compatible region:us
---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- massive
metrics:
- accuracy
- f1
model-index:
- name: scenario-KD-SCR-MSV-D2-data-AmazonScience-massive-all_1.1-model-FacebookAI-xlm-r
  results: []
---

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# scenario-KD-SCR-MSV-D2-data-AmazonScience-massive-all_1.1-model-FacebookAI-xlm-r

This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2459
- Accuracy: 0.8556
- F1: 0.8320

## 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: 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 2.0729        | 0.27  | 5000  | 2.1787          | 0.7771   | 0.7137 |
| 1.5892        | 0.53  | 10000 | 1.8314          | 0.8093   | 0.7671 |
| 1.3541        | 0.8   | 15000 | 1.6428          | 0.8254   | 0.7911 |
| 1.0141        | 1.07  | 20000 | 1.5800          | 0.8325   | 0.7922 |
| 0.9443        | 1.34  | 25000 | 1.5117          | 0.8380   | 0.8119 |
| 0.8542        | 1.6   | 30000 | 1.4887          | 0.8404   | 0.8139 |
| 0.8014        | 1.87  | 35000 | 1.4190          | 0.8437   | 0.8190 |
| 0.671         | 2.14  | 40000 | 1.4162          | 0.8459   | 0.8228 |
| 0.6347        | 2.41  | 45000 | 1.3846          | 0.8501   | 0.8248 |
| 0.6126        | 2.67  | 50000 | 1.3772          | 0.8478   | 0.8219 |
| 0.5875        | 2.94  | 55000 | 1.3537          | 0.8488   | 0.8241 |
| 0.4979        | 3.21  | 60000 | 1.3296          | 0.8522   | 0.8302 |
| 0.5045        | 3.47  | 65000 | 1.3210          | 0.8531   | 0.8313 |
| 0.4682        | 3.74  | 70000 | 1.3038          | 0.8535   | 0.8328 |
| 0.4642        | 4.01  | 75000 | 1.2810          | 0.8528   | 0.8323 |
| 0.4218        | 4.28  | 80000 | 1.2822          | 0.8527   | 0.8283 |
| 0.4211        | 4.54  | 85000 | 1.2580          | 0.8544   | 0.8330 |
| 0.4179        | 4.81  | 90000 | 1.2459          | 0.8556   | 0.8320 |


### Framework versions

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