transformers pytorch xlm-roberta text-classification generated_from_trainer dataset:massive base_model:facebook/xlm-v-base base_model:finetune:facebook/xlm-v-base license:mit model-index autotrain_compatible endpoints_compatible region:us
---
base_model: facebook/xlm-v-base
datasets:
- massive
license: mit
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: scenario-TCR-XLMV-4_data-AmazonScience_massive_all_1_1
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: massive
      type: massive
      config: all_1.1
      split: validation
      args: all_1.1
    metrics:
    - type: accuracy
      value: 0.846210601990238
      name: Accuracy
    - type: f1
      value: 0.8244135214839245
      name: F1
---

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# scenario-TCR-XLMV-4_data-AmazonScience_massive_all_1_1

This model is a fine-tuned version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8322
- Accuracy: 0.8462
- F1: 0.8244

## 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: 777
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 500

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.595         | 0.27  | 5000  | 0.7040          | 0.8241   | 0.7720 |
| 0.4654        | 0.53  | 10000 | 0.6468          | 0.8410   | 0.8027 |
| 0.3838        | 0.8   | 15000 | 0.6802          | 0.8399   | 0.7994 |
| 0.2831        | 1.07  | 20000 | 0.7290          | 0.8471   | 0.8206 |
| 0.274         | 1.34  | 25000 | 0.7192          | 0.8471   | 0.8141 |
| 0.2598        | 1.6   | 30000 | 0.7145          | 0.8440   | 0.8215 |
| 0.2501        | 1.87  | 35000 | 0.7347          | 0.8500   | 0.8245 |
| 0.2022        | 2.14  | 40000 | 0.7809          | 0.8503   | 0.8223 |
| 0.2164        | 2.41  | 45000 | 0.7481          | 0.8533   | 0.8280 |
| 0.2008        | 2.67  | 50000 | 0.7684          | 0.8467   | 0.8252 |
| 0.2015        | 2.94  | 55000 | 0.8170          | 0.8422   | 0.8160 |
| 0.1716        | 3.21  | 60000 | 0.8603          | 0.8433   | 0.8186 |
| 0.1643        | 3.47  | 65000 | 0.8221          | 0.8514   | 0.8279 |
| 0.1816        | 3.74  | 70000 | 0.8322          | 0.8462   | 0.8244 |


### Framework versions

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