transformers pytorch xlm-roberta generated_from_trainer dataset:massive base_model:xlm-roberta-base license:mit endpoints_compatible region:us
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- massive
metrics:
- accuracy
- f1
model-index:
- name: scenario-NON-KD-PR-COPY-D2_data-AmazonScience_massive_all_1_1
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# scenario-NON-KD-PR-COPY-D2_data-AmazonScience_massive_all_1_1

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8802
- Accuracy: 0.8560
- F1: 0.8310

## 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: 124
- 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.7484        | 0.27  | 5000  | 0.7906          | 0.7913   | 0.7188 |
| 0.5539        | 0.53  | 10000 | 0.7077          | 0.8158   | 0.7725 |
| 0.462         | 0.8   | 15000 | 0.6492          | 0.8330   | 0.7950 |
| 0.3208        | 1.07  | 20000 | 0.6600          | 0.8431   | 0.8035 |
| 0.289         | 1.34  | 25000 | 0.6522          | 0.8440   | 0.8151 |
| 0.2906        | 1.6   | 30000 | 0.6712          | 0.8443   | 0.8161 |
| 0.2589        | 1.87  | 35000 | 0.6881          | 0.8467   | 0.8206 |
| 0.1838        | 2.14  | 40000 | 0.7388          | 0.8445   | 0.8207 |
| 0.1877        | 2.41  | 45000 | 0.7317          | 0.8494   | 0.8225 |
| 0.1696        | 2.67  | 50000 | 0.7341          | 0.8511   | 0.8226 |
| 0.1786        | 2.94  | 55000 | 0.7350          | 0.8505   | 0.8258 |
| 0.1224        | 3.21  | 60000 | 0.7972          | 0.8517   | 0.8272 |
| 0.1208        | 3.47  | 65000 | 0.7961          | 0.8519   | 0.8271 |
| 0.1145        | 3.74  | 70000 | 0.8196          | 0.8561   | 0.8293 |
| 0.1053        | 4.01  | 75000 | 0.8221          | 0.8564   | 0.8302 |
| 0.0732        | 4.28  | 80000 | 0.8658          | 0.8561   | 0.8322 |
| 0.0811        | 4.54  | 85000 | 0.8759          | 0.8552   | 0.8298 |
| 0.0698        | 4.81  | 90000 | 0.8802          | 0.8560   | 0.8310 |


### Framework versions

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