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
  results: []
---

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# scenario-KD-PR-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: 1.2464
- Accuracy: 0.8555
- F1: 0.8339

## 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 2.1157        | 0.27  | 5000  | 2.2567          | 0.7745   | 0.7027 |
| 1.6203        | 0.53  | 10000 | 1.7883          | 0.8146   | 0.7705 |
| 1.3119        | 0.8   | 15000 | 1.6329          | 0.8265   | 0.7883 |
| 1.0129        | 1.07  | 20000 | 1.5984          | 0.8309   | 0.7962 |
| 0.9375        | 1.34  | 25000 | 1.5647          | 0.8311   | 0.7995 |
| 0.8655        | 1.6   | 30000 | 1.4878          | 0.8371   | 0.8106 |
| 0.8104        | 1.87  | 35000 | 1.4537          | 0.8431   | 0.8184 |
| 0.6635        | 2.14  | 40000 | 1.4188          | 0.8465   | 0.8181 |
| 0.6444        | 2.41  | 45000 | 1.4109          | 0.8438   | 0.8199 |
| 0.6257        | 2.67  | 50000 | 1.3906          | 0.8468   | 0.8249 |
| 0.5824        | 2.94  | 55000 | 1.3424          | 0.8522   | 0.8292 |
| 0.5174        | 3.21  | 60000 | 1.3134          | 0.8530   | 0.8346 |
| 0.5047        | 3.47  | 65000 | 1.3107          | 0.8529   | 0.8302 |
| 0.4781        | 3.74  | 70000 | 1.3045          | 0.8519   | 0.8273 |
| 0.4647        | 4.01  | 75000 | 1.2759          | 0.8526   | 0.8296 |
| 0.4286        | 4.28  | 80000 | 1.2675          | 0.8558   | 0.8349 |
| 0.426         | 4.54  | 85000 | 1.2589          | 0.8539   | 0.8316 |
| 0.4277        | 4.81  | 90000 | 1.2464          | 0.8555   | 0.8339 |


### Framework versions

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