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-PR-MSV-D2-data-AmazonScience-massive-all_1.1-model-FacebookAI-xlm-ro
  results: []
---

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# scenario-KD-PR-MSV-D2-data-AmazonScience-massive-all_1.1-model-FacebookAI-xlm-ro

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: 1.2502
- Accuracy: 0.8559
- F1: 0.8328

## 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 2.1046        | 0.27  | 5000  | 2.1831          | 0.7776   | 0.7077 |
| 1.599         | 0.53  | 10000 | 1.8367          | 0.8094   | 0.7701 |
| 1.3802        | 0.8   | 15000 | 1.6730          | 0.8234   | 0.7840 |
| 1.0199        | 1.07  | 20000 | 1.6027          | 0.8305   | 0.7881 |
| 0.9716        | 1.34  | 25000 | 1.5135          | 0.8374   | 0.8116 |
| 0.8702        | 1.6   | 30000 | 1.4892          | 0.8406   | 0.8171 |
| 0.8061        | 1.87  | 35000 | 1.4375          | 0.8440   | 0.8223 |
| 0.6773        | 2.14  | 40000 | 1.4357          | 0.8439   | 0.8235 |
| 0.634         | 2.41  | 45000 | 1.4174          | 0.8477   | 0.8217 |
| 0.6223        | 2.67  | 50000 | 1.3628          | 0.8518   | 0.8271 |
| 0.5918        | 2.94  | 55000 | 1.3634          | 0.8481   | 0.8258 |
| 0.5001        | 3.21  | 60000 | 1.3337          | 0.8506   | 0.8272 |
| 0.5123        | 3.47  | 65000 | 1.3183          | 0.8520   | 0.8324 |
| 0.47          | 3.74  | 70000 | 1.3076          | 0.8527   | 0.8314 |
| 0.4599        | 4.01  | 75000 | 1.2786          | 0.8545   | 0.8339 |
| 0.4269        | 4.28  | 80000 | 1.2758          | 0.8536   | 0.8280 |
| 0.4256        | 4.54  | 85000 | 1.2627          | 0.8539   | 0.8317 |
| 0.413         | 4.81  | 90000 | 1.2502          | 0.8559   | 0.8328 |


### Framework versions

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