---
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: 0.4972993827160494
    - name: F1
      type: f1
      value: 0.49564146924204383
---

<!-- 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: 5.7429
- Accuracy: 0.4973
- F1: 0.4956

## 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: 222
- 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 1.1068        | 1.09  | 500   | 1.0681          | 0.4352   | 0.4321 |
| 0.9081        | 2.17  | 1000  | 1.2631          | 0.5046   | 0.5021 |
| 0.5532        | 3.26  | 1500  | 1.5304          | 0.5108   | 0.5089 |
| 0.2998        | 4.35  | 2000  | 2.0584          | 0.4884   | 0.4858 |
| 0.1717        | 5.43  | 2500  | 2.7362          | 0.5      | 0.4939 |
| 0.1242        | 6.52  | 3000  | 3.0470          | 0.4969   | 0.4938 |
| 0.0874        | 7.61  | 3500  | 2.7990          | 0.5046   | 0.5037 |
| 0.0669        | 8.7   | 4000  | 3.2793          | 0.4942   | 0.4940 |
| 0.056         | 9.78  | 4500  | 3.2094          | 0.5027   | 0.5028 |
| 0.0487        | 10.87 | 5000  | 3.5054          | 0.4992   | 0.4972 |
| 0.0539        | 11.96 | 5500  | 3.2798          | 0.5008   | 0.5003 |
| 0.0317        | 13.04 | 6000  | 3.4251          | 0.5004   | 0.4994 |
| 0.0449        | 14.13 | 6500  | 4.0353          | 0.4969   | 0.4923 |
| 0.0303        | 15.22 | 7000  | 4.3157          | 0.4850   | 0.4733 |
| 0.0285        | 16.3  | 7500  | 3.8740          | 0.4985   | 0.4987 |
| 0.0214        | 17.39 | 8000  | 4.5553          | 0.4842   | 0.4828 |
| 0.0228        | 18.48 | 8500  | 4.7444          | 0.4946   | 0.4903 |
| 0.0177        | 19.57 | 9000  | 4.5373          | 0.4969   | 0.4939 |
| 0.0167        | 20.65 | 9500  | 4.4792          | 0.4927   | 0.4859 |
| 0.0144        | 21.74 | 10000 | 4.6491          | 0.4896   | 0.4897 |
| 0.0164        | 22.83 | 10500 | 4.8310          | 0.4934   | 0.4926 |
| 0.0116        | 23.91 | 11000 | 4.6267          | 0.4996   | 0.4965 |
| 0.0102        | 25.0  | 11500 | 5.0420          | 0.4904   | 0.4808 |
| 0.0053        | 26.09 | 12000 | 5.2202          | 0.4915   | 0.4824 |
| 0.01          | 27.17 | 12500 | 4.8786          | 0.4900   | 0.4868 |
| 0.0076        | 28.26 | 13000 | 4.8830          | 0.4919   | 0.4906 |
| 0.0064        | 29.35 | 13500 | 5.2319          | 0.4934   | 0.4890 |
| 0.0055        | 30.43 | 14000 | 5.4810          | 0.4973   | 0.4953 |
| 0.0057        | 31.52 | 14500 | 5.4109          | 0.5035   | 0.5019 |
| 0.0032        | 32.61 | 15000 | 5.3979          | 0.5054   | 0.5041 |
| 0.0092        | 33.7  | 15500 | 5.3848          | 0.4942   | 0.4940 |
| 0.0053        | 34.78 | 16000 | 5.2937          | 0.5066   | 0.5046 |
| 0.0029        | 35.87 | 16500 | 5.5430          | 0.5012   | 0.4971 |
| 0.0011        | 36.96 | 17000 | 5.6338          | 0.4919   | 0.4905 |
| 0.0027        | 38.04 | 17500 | 5.6234          | 0.4958   | 0.4960 |
| 0.0042        | 39.13 | 18000 | 5.5802          | 0.4988   | 0.4991 |
| 0.0012        | 40.22 | 18500 | 5.6464          | 0.4988   | 0.4993 |
| 0.0037        | 41.3  | 19000 | 5.6227          | 0.4965   | 0.4945 |
| 0.0007        | 42.39 | 19500 | 5.6263          | 0.4958   | 0.4939 |
| 0.0003        | 43.48 | 20000 | 5.6946          | 0.4934   | 0.4937 |
| 0.0016        | 44.57 | 20500 | 5.6654          | 0.4973   | 0.4977 |
| 0.0018        | 45.65 | 21000 | 5.6725          | 0.4965   | 0.4952 |
| 0.0012        | 46.74 | 21500 | 5.6500          | 0.4873   | 0.4869 |
| 0.0008        | 47.83 | 22000 | 5.6626          | 0.4992   | 0.4985 |
| 0.0006        | 48.91 | 22500 | 5.7378          | 0.4985   | 0.4968 |
| 0.0004        | 50.0  | 23000 | 5.7429          | 0.4973   | 0.4956 |


### Framework versions

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