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-1_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: 0.8472984221877483
      name: Accuracy
    - type: f1
      value: 0.8225956665149763
      name: F1
---

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# scenario-TCR-XLMV-1_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: 0.7886
- Accuracy: 0.8473
- F1: 0.8226

## 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: 500

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.587         | 0.27  | 5000  | 0.7148          | 0.8166   | 0.7696 |
| 0.456         | 0.53  | 10000 | 0.6624          | 0.8415   | 0.8006 |
| 0.3711        | 0.8   | 15000 | 0.6803          | 0.8394   | 0.8064 |
| 0.2846        | 1.07  | 20000 | 0.7409          | 0.8406   | 0.8119 |
| 0.2698        | 1.34  | 25000 | 0.7120          | 0.8428   | 0.8129 |
| 0.2589        | 1.6   | 30000 | 0.7179          | 0.8478   | 0.8300 |
| 0.246         | 1.87  | 35000 | 0.7383          | 0.8455   | 0.8119 |
| 0.2079        | 2.14  | 40000 | 0.7911          | 0.8503   | 0.8162 |
| 0.2157        | 2.41  | 45000 | 0.7775          | 0.8434   | 0.8251 |
| 0.2111        | 2.67  | 50000 | 0.7737          | 0.8455   | 0.8196 |
| 0.2014        | 2.94  | 55000 | 0.7886          | 0.8473   | 0.8226 |


### Framework versions

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