---
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_delta2
  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: 0.051647811116576486
    - name: F1
      type: f1
      value: 0.0016647904742274576
---

<!-- 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_delta2

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: 3.8750
- Accuracy: 0.0516
- F1: 0.0017

## 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: 2
- 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 3.7486        | 0.27  | 5000  | 3.7603          | 0.0620   | 0.0020 |
| 3.7278        | 0.53  | 10000 | 3.8114          | 0.0620   | 0.0020 |
| 3.7049        | 0.8   | 15000 | 3.8427          | 0.0516   | 0.0017 |
| 3.7157        | 1.07  | 20000 | 3.8730          | 0.0516   | 0.0017 |
| 3.7141        | 1.34  | 25000 | 3.8796          | 0.0516   | 0.0017 |
| 3.699         | 1.6   | 30000 | 3.8750          | 0.0516   | 0.0017 |


### Framework versions

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