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
  results: []
---

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# scenario-KD-SCR-MSV-D2-data-AmazonScience-massive-all_1.1

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: 2.0367
- Accuracy: 0.8121
- F1: 0.7792

## 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 5.1774        | 0.27  | 5000  | 5.0790          | 0.4846   | 0.3131 |
| 3.6724        | 0.53  | 10000 | 3.6189          | 0.6568   | 0.5466 |
| 2.9901        | 0.8   | 15000 | 3.0326          | 0.7140   | 0.6389 |
| 2.3261        | 1.07  | 20000 | 2.7942          | 0.7392   | 0.6645 |
| 2.1941        | 1.34  | 25000 | 2.5807          | 0.7588   | 0.6987 |
| 2.0838        | 1.6   | 30000 | 2.4364          | 0.7730   | 0.7278 |
| 2.0113        | 1.87  | 35000 | 2.2993          | 0.7877   | 0.7343 |
| 1.5771        | 2.14  | 40000 | 2.2642          | 0.7910   | 0.7474 |
| 1.5643        | 2.41  | 45000 | 2.2360          | 0.7936   | 0.7492 |
| 1.5229        | 2.67  | 50000 | 2.1530          | 0.7979   | 0.7577 |
| 1.4509        | 2.94  | 55000 | 2.0710          | 0.8051   | 0.7618 |
| 1.2153        | 3.21  | 60000 | 2.1402          | 0.8025   | 0.7665 |
| 1.1898        | 3.47  | 65000 | 2.0841          | 0.8049   | 0.7696 |
| 1.152         | 3.74  | 70000 | 2.0548          | 0.8069   | 0.7714 |
| 1.0835        | 4.01  | 75000 | 2.0480          | 0.8116   | 0.7741 |
| 0.9711        | 4.28  | 80000 | 2.0666          | 0.8103   | 0.7774 |
| 0.9538        | 4.54  | 85000 | 2.0670          | 0.8113   | 0.7772 |
| 0.9557        | 4.81  | 90000 | 2.0367          | 0.8121   | 0.7792 |


### Framework versions

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