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_data-AmazonScience_massive_all_1_1_beta
  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: 0.051647811116576486
      name: Accuracy
    - type: f1
      value: 0.0016647904742274576
      name: F1
---

<!-- 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_beta

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: 3.8507
- 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: 112233
- 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 3.7597        | 0.27  | 5000  | 3.7356          | 0.0644   | 0.0021 |
| 3.74          | 0.53  | 10000 | 3.7433          | 0.0620   | 0.0020 |
| 3.7286        | 0.8   | 15000 | 3.7729          | 0.0620   | 0.0020 |
| 3.7156        | 1.07  | 20000 | 3.8497          | 0.0516   | 0.0017 |
| 3.7167        | 1.34  | 25000 | 3.8316          | 0.0516   | 0.0017 |
| 3.7147        | 1.6   | 30000 | 3.8507          | 0.0516   | 0.0017 |


### Framework versions

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