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# scenario-kd-from-scratch-gold-silver-data-tyqiangz-multilingual-sentiments-engli

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: 5.2973
- Accuracy: 0.3148
- F1: 0.2944

## 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     |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|
| 3.7402        | 17.24  | 1000  | 6.1754          | 0.2222   | 0.2367 |
| 3.3823        | 34.48  | 2000  | 5.4147          | 0.2747   | 0.2589 |
| 3.2883        | 51.72  | 3000  | 5.3761          | 0.2932   | 0.2718 |
| 3.2195        | 68.97  | 4000  | 5.3599          | 0.2840   | 0.2655 |
| 3.1644        | 86.21  | 5000  | 5.2017          | 0.3025   | 0.2703 |
| 3.122         | 103.45 | 6000  | 5.1818          | 0.3302   | 0.2906 |
| 3.0816        | 120.69 | 7000  | 5.2311          | 0.2994   | 0.2824 |
| 3.0464        | 137.93 | 8000  | 5.1503          | 0.3117   | 0.2730 |
| 3.0209        | 155.17 | 9000  | 5.2700          | 0.3210   | 0.3024 |
| 2.9862        | 172.41 | 10000 | 5.2179          | 0.3241   | 0.3070 |
| 2.9634        | 189.66 | 11000 | 5.2879          | 0.3210   | 0.3114 |
| 2.9459        | 206.9  | 12000 | 5.1983          | 0.3364   | 0.3162 |
| 2.9261        | 224.14 | 13000 | 5.1687          | 0.3148   | 0.2994 |
| 2.9087        | 241.38 | 14000 | 5.2242          | 0.3302   | 0.3127 |
| 2.9006        | 258.62 | 15000 | 5.1903          | 0.3210   | 0.2966 |
| 2.888         | 275.86 | 16000 | 5.1641          | 0.3210   | 0.3012 |
| 2.8704        | 293.1  | 17000 | 5.2025          | 0.3210   | 0.3023 |
| 2.8726        | 310.34 | 18000 | 5.1855          | 0.3056   | 0.2842 |
| 2.8591        | 327.59 | 19000 | 5.2598          | 0.3210   | 0.2990 |
| 2.857         | 344.83 | 20000 | 5.2402          | 0.3086   | 0.2940 |
| 2.8491        | 362.07 | 21000 | 5.2596          | 0.3364   | 0.3125 |
| 2.8444        | 379.31 | 22000 | 5.2973          | 0.3148   | 0.2944 |


### 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-kd-from-scratch-gold-silver-data-tyqiangz-multilingual-sentiments-engli", "results": []}]}
transformers pytorch xlm-roberta generated_from_trainer dataset:multilingual-sentiments base_model:xlm-roberta-base license:mit endpoints_compatible region:us