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-POST-DIV4-data-AmazonScience-massive-all_1.1
  results: []
---

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# scenario-KD-POST-DIV4-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: 1.4904
- Accuracy: 0.8419
- F1: 0.8176

## 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 2.7199        | 0.27  | 5000  | 2.7114          | 0.7182   | 0.6195 |
| 2.1073        | 0.53  | 10000 | 2.1949          | 0.7723   | 0.7024 |
| 1.8315        | 0.8   | 15000 | 1.9562          | 0.7978   | 0.7459 |
| 1.4392        | 1.07  | 20000 | 1.8530          | 0.8077   | 0.7624 |
| 1.3819        | 1.34  | 25000 | 1.7610          | 0.8166   | 0.7761 |
| 1.2627        | 1.6   | 30000 | 1.6937          | 0.8228   | 0.7852 |
| 1.1959        | 1.87  | 35000 | 1.6270          | 0.8268   | 0.7933 |
| 1.0143        | 2.14  | 40000 | 1.6256          | 0.8275   | 0.7932 |
| 0.9642        | 2.41  | 45000 | 1.6037          | 0.8314   | 0.7983 |
| 0.9476        | 2.67  | 50000 | 1.5667          | 0.8338   | 0.8012 |
| 0.9108        | 2.94  | 55000 | 1.5477          | 0.8355   | 0.8049 |
| 0.7792        | 3.21  | 60000 | 1.5534          | 0.8364   | 0.8091 |
| 0.7739        | 3.47  | 65000 | 1.5446          | 0.8374   | 0.8102 |
| 0.7448        | 3.74  | 70000 | 1.5203          | 0.8396   | 0.8136 |
| 0.7206        | 4.01  | 75000 | 1.5081          | 0.8396   | 0.8150 |
| 0.6785        | 4.28  | 80000 | 1.5130          | 0.8399   | 0.8134 |
| 0.6704        | 4.54  | 85000 | 1.5071          | 0.8405   | 0.8153 |
| 0.6575        | 4.81  | 90000 | 1.4904          | 0.8419   | 0.8176 |


### Framework versions

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