---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- tweet_sentiment_multilingual
metrics:
- accuracy
- f1
model-index:
- name: scenario-NON-KD-SCR-COPY-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: tweet_sentiment_multilingual
      type: tweet_sentiment_multilingual
      config: all
      split: validation
      args: all
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.47762345679012347
    - name: F1
      type: f1
      value: 0.47819062529207484
---

<!-- 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-NON-KD-SCR-COPY-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual

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: 6.0055
- Accuracy: 0.4776
- F1: 0.4782

## 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: 11423
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 1.1157        | 1.09  | 500   | 1.0964          | 0.3835   | 0.2965 |
| 0.9636        | 2.17  | 1000  | 1.1184          | 0.4954   | 0.4470 |
| 0.5977        | 3.26  | 1500  | 1.4984          | 0.5116   | 0.5070 |
| 0.342         | 4.35  | 2000  | 1.8178          | 0.5077   | 0.5054 |
| 0.1946        | 5.43  | 2500  | 2.5918          | 0.5077   | 0.5062 |
| 0.1442        | 6.52  | 3000  | 2.5451          | 0.4904   | 0.4833 |
| 0.101         | 7.61  | 3500  | 3.3273          | 0.4942   | 0.4879 |
| 0.0788        | 8.7   | 4000  | 3.3097          | 0.4811   | 0.4729 |
| 0.0596        | 9.78  | 4500  | 3.4639          | 0.4954   | 0.4959 |
| 0.0505        | 10.87 | 5000  | 3.5381          | 0.4884   | 0.4884 |
| 0.0413        | 11.96 | 5500  | 3.3937          | 0.4958   | 0.4961 |
| 0.0364        | 13.04 | 6000  | 3.9058          | 0.4850   | 0.4848 |
| 0.0273        | 14.13 | 6500  | 4.3025          | 0.4892   | 0.4887 |
| 0.0282        | 15.22 | 7000  | 3.9833          | 0.4877   | 0.4885 |
| 0.0253        | 16.3  | 7500  | 4.4515          | 0.4811   | 0.4802 |
| 0.0188        | 17.39 | 8000  | 4.7345          | 0.4873   | 0.4843 |
| 0.0191        | 18.48 | 8500  | 4.5842          | 0.4880   | 0.4880 |
| 0.0187        | 19.57 | 9000  | 4.6871          | 0.4838   | 0.4821 |
| 0.0189        | 20.65 | 9500  | 4.7307          | 0.4931   | 0.4857 |
| 0.0157        | 21.74 | 10000 | 4.8938          | 0.4796   | 0.4722 |
| 0.0133        | 22.83 | 10500 | 4.6099          | 0.4765   | 0.4681 |
| 0.0107        | 23.91 | 11000 | 5.0670          | 0.4815   | 0.4787 |
| 0.0076        | 25.0  | 11500 | 4.9710          | 0.4799   | 0.4780 |
| 0.0078        | 26.09 | 12000 | 5.0339          | 0.4830   | 0.4841 |
| 0.0101        | 27.17 | 12500 | 5.0560          | 0.4904   | 0.4907 |
| 0.0086        | 28.26 | 13000 | 5.0095          | 0.4850   | 0.4843 |
| 0.0074        | 29.35 | 13500 | 5.1031          | 0.4846   | 0.4831 |
| 0.0032        | 30.43 | 14000 | 5.4537          | 0.4830   | 0.4840 |
| 0.0054        | 31.52 | 14500 | 5.4554          | 0.4838   | 0.4847 |
| 0.0046        | 32.61 | 15000 | 5.5972          | 0.4780   | 0.4774 |
| 0.0059        | 33.7  | 15500 | 5.3884          | 0.4853   | 0.4863 |
| 0.0029        | 34.78 | 16000 | 5.3174          | 0.4738   | 0.4736 |
| 0.0033        | 35.87 | 16500 | 5.5911          | 0.4753   | 0.4742 |
| 0.0041        | 36.96 | 17000 | 5.2149          | 0.4769   | 0.4747 |
| 0.0034        | 38.04 | 17500 | 5.5052          | 0.4857   | 0.4853 |
| 0.0014        | 39.13 | 18000 | 5.5164          | 0.4807   | 0.4812 |
| 0.0015        | 40.22 | 18500 | 5.6182          | 0.4803   | 0.4791 |
| 0.0002        | 41.3  | 19000 | 5.7053          | 0.4799   | 0.4780 |
| 0.0001        | 42.39 | 19500 | 5.7820          | 0.4826   | 0.4808 |
| 0.0001        | 43.48 | 20000 | 5.8324          | 0.4850   | 0.4844 |
| 0.0005        | 44.57 | 20500 | 5.9002          | 0.4823   | 0.4798 |
| 0.0004        | 45.65 | 21000 | 5.9340          | 0.4811   | 0.4810 |
| 0.0011        | 46.74 | 21500 | 5.9656          | 0.4780   | 0.4785 |
| 0.0002        | 47.83 | 22000 | 5.9859          | 0.4792   | 0.4798 |
| 0.0001        | 48.91 | 22500 | 5.9994          | 0.4788   | 0.4793 |
| 0.0001        | 50.0  | 23000 | 6.0055          | 0.4776   | 0.4782 |


### Framework versions

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