---
license: mit
base_model: facebook/xlm-v-base
tags:
- generated_from_trainer
datasets:
- massive
metrics:
- accuracy
- f1
model-index:
- name: scenario-TCR-XLMV_data-AmazonScience_massive_all_1_1_beta2
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: massive
      type: massive
      config: all_1.1
      split: validation
      args: all_1.1
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8495213591130955
    - name: F1
      type: f1
      value: 0.8257523979629272
---

<!-- 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-TCR-XLMV_data-AmazonScience_massive_all_1_1_beta2

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: 0.8678
- Accuracy: 0.8495
- F1: 0.8258

## 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: 67
- 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     |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|
| 0.6252        | 0.27  | 5000   | 0.7387          | 0.8183   | 0.7743 |
| 0.4497        | 0.53  | 10000  | 0.6721          | 0.8363   | 0.7908 |
| 0.3806        | 0.8   | 15000  | 0.6702          | 0.8451   | 0.8090 |
| 0.303         | 1.07  | 20000  | 0.7162          | 0.8457   | 0.8130 |
| 0.2732        | 1.34  | 25000  | 0.7250          | 0.8475   | 0.8178 |
| 0.2574        | 1.6   | 30000  | 0.7626          | 0.8449   | 0.8188 |
| 0.2565        | 1.87  | 35000  | 0.7255          | 0.8506   | 0.8251 |
| 0.2074        | 2.14  | 40000  | 0.7439          | 0.8524   | 0.8268 |
| 0.2139        | 2.41  | 45000  | 0.8088          | 0.8478   | 0.8233 |
| 0.2007        | 2.67  | 50000  | 0.7556          | 0.8476   | 0.8223 |
| 0.2012        | 2.94  | 55000  | 0.7599          | 0.8505   | 0.8250 |
| 0.1698        | 3.21  | 60000  | 0.8283          | 0.8481   | 0.8255 |
| 0.1728        | 3.47  | 65000  | 0.7996          | 0.8521   | 0.8320 |
| 0.1711        | 3.74  | 70000  | 0.7974          | 0.8520   | 0.8292 |
| 0.1623        | 4.01  | 75000  | 0.8819          | 0.8485   | 0.8223 |
| 0.1502        | 4.28  | 80000  | 0.8330          | 0.8534   | 0.8320 |
| 0.1605        | 4.54  | 85000  | 0.8250          | 0.8499   | 0.8264 |
| 0.1659        | 4.81  | 90000  | 0.8318          | 0.8493   | 0.8237 |
| 0.1241        | 5.08  | 95000  | 0.9368          | 0.8518   | 0.8191 |
| 0.1361        | 5.34  | 100000 | 0.9396          | 0.8510   | 0.8237 |
| 0.1481        | 5.61  | 105000 | 0.8678          | 0.8495   | 0.8258 |


### Framework versions

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