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# scenario-non-kd-from-scratch-data-tyqiangz-multilingual-sentiments-malay-model-x

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the multilingual-sentiments dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0493
- Accuracy: 0.6239
- F1: 0.5869

## 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: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6969

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 0.68  | 100  | 0.8916          | 0.4915   | 0.3639 |
| No log        | 1.36  | 200  | 0.8477          | 0.5771   | 0.4958 |
| No log        | 2.04  | 300  | 0.9598          | 0.5771   | 0.4267 |
| No log        | 2.72  | 400  | 0.8090          | 0.6010   | 0.5801 |
| 0.7834        | 3.4   | 500  | 0.8969          | 0.6189   | 0.5971 |
| 0.7834        | 4.08  | 600  | 1.0623          | 0.6368   | 0.6102 |
| 0.7834        | 4.76  | 700  | 1.3065          | 0.6060   | 0.5366 |
| 0.7834        | 5.44  | 800  | 1.6245          | 0.6179   | 0.5417 |
| 0.7834        | 6.12  | 900  | 1.9429          | 0.5940   | 0.4724 |
| 0.2823        | 6.8   | 1000 | 1.5144          | 0.6159   | 0.5910 |
| 0.2823        | 7.48  | 1100 | 1.6368          | 0.6458   | 0.6231 |
| 0.2823        | 8.16  | 1200 | 2.0009          | 0.5831   | 0.5738 |
| 0.2823        | 8.84  | 1300 | 2.0191          | 0.6100   | 0.5634 |
| 0.2823        | 9.52  | 1400 | 2.1917          | 0.6388   | 0.5793 |
| 0.1213        | 10.2  | 1500 | 2.0430          | 0.6348   | 0.5901 |
| 0.1213        | 10.88 | 1600 | 2.6876          | 0.5980   | 0.5471 |
| 0.1213        | 11.56 | 1700 | 2.6026          | 0.6358   | 0.5932 |
| 0.1213        | 12.24 | 1800 | 2.8122          | 0.6129   | 0.5579 |
| 0.1213        | 12.93 | 1900 | 2.6769          | 0.6189   | 0.5731 |
| 0.0555        | 13.61 | 2000 | 2.6787          | 0.6418   | 0.5997 |
| 0.0555        | 14.29 | 2100 | 3.0463          | 0.6100   | 0.5937 |
| 0.0555        | 14.97 | 2200 | 2.8793          | 0.6428   | 0.6061 |
| 0.0555        | 15.65 | 2300 | 3.0057          | 0.6259   | 0.5970 |
| 0.0555        | 16.33 | 2400 | 3.0933          | 0.6468   | 0.6181 |
| 0.0363        | 17.01 | 2500 | 3.0729          | 0.6428   | 0.6067 |
| 0.0363        | 17.69 | 2600 | 3.0493          | 0.6239   | 0.5869 |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["multilingual-sentiments"], "metrics": ["accuracy", "f1"], "base_model": "xlm-roberta-base", "model-index": [{"name": "scenario-non-kd-from-scratch-data-tyqiangz-multilingual-sentiments-malay-model-x", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "multilingual-sentiments", "type": "multilingual-sentiments", "config": "malay", "split": "validation", "args": "malay"}, "metrics": [{"type": "accuracy", "value": 0.6238805970149254, "name": "Accuracy"}, {"type": "f1", "value": 0.5868956366785256, "name": "F1"}]}]}]}
transformers pytorch xlm-roberta text-classification generated_from_trainer dataset:multilingual-sentiments base_model:xlm-roberta-base license:mit model-index autotrain_compatible endpoints_compatible region:us