
<!-- 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-kd-from-scratch-gold-silver-data-tyqiangz-multilingual-sentiments-malay

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: 17.6857
- Accuracy: 0.5980
- F1: 0.5654

## 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  | 14.0474         | 0.4905   | 0.3316 |
| No log        | 1.36  | 200  | 12.2834         | 0.5453   | 0.4729 |
| No log        | 2.04  | 300  | 12.4374         | 0.5861   | 0.4237 |
| No log        | 2.72  | 400  | 13.2619         | 0.5741   | 0.5227 |
| 13.7796       | 3.4   | 500  | 12.1854         | 0.6129   | 0.5731 |
| 13.7796       | 4.08  | 600  | 12.8164         | 0.6040   | 0.5276 |
| 13.7796       | 4.76  | 700  | 12.4884         | 0.6219   | 0.5756 |
| 13.7796       | 5.44  | 800  | 12.8795         | 0.6438   | 0.5998 |
| 13.7796       | 6.12  | 900  | 14.1442         | 0.6020   | 0.5644 |
| 7.9325        | 6.8   | 1000 | 12.7533         | 0.6418   | 0.5732 |
| 7.9325        | 7.48  | 1100 | 15.2445         | 0.6189   | 0.5581 |
| 7.9325        | 8.16  | 1200 | 14.9523         | 0.6229   | 0.5235 |
| 7.9325        | 8.84  | 1300 | 13.7360         | 0.6398   | 0.6106 |
| 7.9325        | 9.52  | 1400 | 15.1393         | 0.6318   | 0.5720 |
| 4.2359        | 10.2  | 1500 | 15.9783         | 0.6259   | 0.5741 |
| 4.2359        | 10.88 | 1600 | 14.7346         | 0.6418   | 0.5991 |
| 4.2359        | 11.56 | 1700 | 15.2632         | 0.6448   | 0.6049 |
| 4.2359        | 12.24 | 1800 | 17.0193         | 0.6259   | 0.5391 |
| 4.2359        | 12.93 | 1900 | 16.8662         | 0.6338   | 0.5390 |
| 2.918         | 13.61 | 2000 | 16.0299         | 0.6259   | 0.5909 |
| 2.918         | 14.29 | 2100 | 15.7952         | 0.6398   | 0.5860 |
| 2.918         | 14.97 | 2200 | 16.8918         | 0.6279   | 0.5861 |
| 2.918         | 15.65 | 2300 | 17.1674         | 0.6199   | 0.5401 |
| 2.918         | 16.33 | 2400 | 15.7054         | 0.6488   | 0.6055 |
| 2.1481        | 17.01 | 2500 | 18.5495         | 0.6229   | 0.5230 |
| 2.1481        | 17.69 | 2600 | 17.0136         | 0.6209   | 0.5990 |
| 2.1481        | 18.37 | 2700 | 16.1657         | 0.6517   | 0.6189 |
| 2.1481        | 19.05 | 2800 | 16.8711         | 0.6358   | 0.5855 |
| 2.1481        | 19.73 | 2900 | 17.0981         | 0.6199   | 0.5547 |
| 2.2033        | 20.41 | 3000 | 14.4731         | 0.6547   | 0.6180 |
| 2.2033        | 21.09 | 3100 | 15.9588         | 0.6219   | 0.5892 |
| 2.2033        | 21.77 | 3200 | 14.9411         | 0.6587   | 0.5949 |
| 2.2033        | 22.45 | 3300 | 14.4769         | 0.6577   | 0.6194 |
| 2.2033        | 23.13 | 3400 | 15.6655         | 0.6498   | 0.5920 |
| 1.7483        | 23.81 | 3500 | 16.0186         | 0.6408   | 0.5611 |
| 1.7483        | 24.49 | 3600 | 16.1892         | 0.6299   | 0.5984 |
| 1.7483        | 25.17 | 3700 | 15.2595         | 0.6537   | 0.6043 |
| 1.7483        | 25.85 | 3800 | 15.8051         | 0.6478   | 0.5952 |
| 1.7483        | 26.53 | 3900 | 17.0388         | 0.6070   | 0.5848 |
| 1.5048        | 27.21 | 4000 | 15.9968         | 0.6229   | 0.5960 |
| 1.5048        | 27.89 | 4100 | 15.2344         | 0.6577   | 0.6054 |
| 1.5048        | 28.57 | 4200 | 15.1395         | 0.6348   | 0.5959 |
| 1.5048        | 29.25 | 4300 | 16.6482         | 0.6269   | 0.6032 |
| 1.5048        | 29.93 | 4400 | 16.4803         | 0.6388   | 0.5881 |
| 1.5532        | 30.61 | 4500 | 14.8284         | 0.6667   | 0.6189 |
| 1.5532        | 31.29 | 4600 | 14.4655         | 0.6498   | 0.5978 |
| 1.5532        | 31.97 | 4700 | 17.3168         | 0.6189   | 0.5960 |
| 1.5532        | 32.65 | 4800 | 17.6857         | 0.5980   | 0.5654 |


### 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-malay", "results": []}]}
transformers pytorch xlm-roberta generated_from_trainer dataset:multilingual-sentiments base_model:xlm-roberta-base license:mit endpoints_compatible region:us