transformers pytorch xlm-roberta text-classification generated_from_trainer dataset:tweet_sentiment_multilingual base_model:FacebookAI/xlm-roberta-base base_model:finetune:FacebookAI/xlm-roberta-base license:mit model-index autotrain_compatible endpoints_compatible region:us
---
base_model: xlm-roberta-base
datasets:
- tweet_sentiment_multilingual
license: mit
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: scenario-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all_b
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: tweet_sentiment_multilingual
      type: tweet_sentiment_multilingual
      config: all
      split: validation
      args: all
    metrics:
    - type: accuracy
      value: 0.6373456790123457
      name: Accuracy
    - type: f1
      value: 0.6373299123648605
      name: F1
---

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# scenario-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all_b

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the tweet_sentiment_multilingual dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7148
- Accuracy: 0.6373
- F1: 0.6373

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 6666
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.9727        | 1.09  | 500   | 0.8537          | 0.6134   | 0.6083 |
| 0.8035        | 2.17  | 1000  | 0.8204          | 0.6312   | 0.6114 |
| 0.7087        | 3.26  | 1500  | 0.8422          | 0.6393   | 0.6272 |
| 0.6246        | 4.35  | 2000  | 0.8451          | 0.6431   | 0.6369 |
| 0.5428        | 5.43  | 2500  | 0.9453          | 0.6412   | 0.6399 |
| 0.4829        | 6.52  | 3000  | 1.0229          | 0.6431   | 0.6404 |
| 0.4161        | 7.61  | 3500  | 0.9831          | 0.6478   | 0.6473 |
| 0.3612        | 8.7   | 4000  | 1.1288          | 0.6400   | 0.6395 |
| 0.3086        | 9.78  | 4500  | 1.2783          | 0.6335   | 0.6326 |
| 0.2779        | 10.87 | 5000  | 1.3253          | 0.625    | 0.6229 |
| 0.244         | 11.96 | 5500  | 1.4854          | 0.6404   | 0.6388 |
| 0.2192        | 13.04 | 6000  | 1.5661          | 0.6308   | 0.6331 |
| 0.1881        | 14.13 | 6500  | 1.7309          | 0.6304   | 0.6282 |
| 0.1654        | 15.22 | 7000  | 1.8752          | 0.6316   | 0.6298 |
| 0.162         | 16.3  | 7500  | 1.7038          | 0.6377   | 0.6390 |
| 0.1392        | 17.39 | 8000  | 1.8729          | 0.6219   | 0.6226 |
| 0.1234        | 18.48 | 8500  | 1.9495          | 0.6312   | 0.6317 |
| 0.1125        | 19.57 | 9000  | 2.1487          | 0.6308   | 0.6334 |
| 0.1035        | 20.65 | 9500  | 2.1859          | 0.6404   | 0.6406 |
| 0.0988        | 21.74 | 10000 | 2.2385          | 0.6281   | 0.6277 |
| 0.0837        | 22.83 | 10500 | 2.3919          | 0.6377   | 0.6383 |
| 0.0793        | 23.91 | 11000 | 2.5023          | 0.6408   | 0.6420 |
| 0.0773        | 25.0  | 11500 | 2.5799          | 0.6319   | 0.6316 |
| 0.0724        | 26.09 | 12000 | 2.6326          | 0.6327   | 0.6335 |
| 0.0659        | 27.17 | 12500 | 2.7253          | 0.6335   | 0.6336 |
| 0.0589        | 28.26 | 13000 | 2.7358          | 0.6343   | 0.6345 |
| 0.0613        | 29.35 | 13500 | 2.7148          | 0.6373   | 0.6373 |


### Framework versions

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