
<!-- 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-from-pre-finetune-div-2-data-tyqiangz-multilingual-sentiments-mo

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: 2.1725
- Accuracy: 0.7254
- F1: 0.6927

## 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  | 0.7265          | 0.7045   | 0.6870 |
| No log        | 1.36  | 200  | 0.6970          | 0.7323   | 0.6944 |
| No log        | 2.04  | 300  | 0.6317          | 0.7294   | 0.7067 |
| No log        | 2.72  | 400  | 0.6540          | 0.7413   | 0.7021 |
| 0.5885        | 3.4   | 500  | 0.8806          | 0.7075   | 0.6671 |
| 0.5885        | 4.08  | 600  | 0.9042          | 0.7154   | 0.6895 |
| 0.5885        | 4.76  | 700  | 0.7679          | 0.7443   | 0.7145 |
| 0.5885        | 5.44  | 800  | 0.8522          | 0.7493   | 0.7300 |
| 0.5885        | 6.12  | 900  | 0.9323          | 0.7453   | 0.7172 |
| 0.2592        | 6.8   | 1000 | 1.1891          | 0.7164   | 0.6874 |
| 0.2592        | 7.48  | 1100 | 1.3899          | 0.7234   | 0.6985 |
| 0.2592        | 8.16  | 1200 | 1.4546          | 0.7343   | 0.7045 |
| 0.2592        | 8.84  | 1300 | 1.2937          | 0.7274   | 0.7065 |
| 0.2592        | 9.52  | 1400 | 1.4545          | 0.7303   | 0.6893 |
| 0.1436        | 10.2  | 1500 | 1.5054          | 0.7363   | 0.7036 |
| 0.1436        | 10.88 | 1600 | 1.5872          | 0.7303   | 0.6985 |
| 0.1436        | 11.56 | 1700 | 1.3485          | 0.7453   | 0.7255 |
| 0.1436        | 12.24 | 1800 | 1.5478          | 0.7632   | 0.7398 |
| 0.1436        | 12.93 | 1900 | 1.2108          | 0.7443   | 0.7233 |
| 0.1044        | 13.61 | 2000 | 1.3613          | 0.7642   | 0.7397 |
| 0.1044        | 14.29 | 2100 | 1.7515          | 0.7572   | 0.7307 |
| 0.1044        | 14.97 | 2200 | 1.7570          | 0.7552   | 0.7278 |
| 0.1044        | 15.65 | 2300 | 1.6992          | 0.7323   | 0.7119 |
| 0.1044        | 16.33 | 2400 | 2.0103          | 0.7254   | 0.6996 |
| 0.0789        | 17.01 | 2500 | 1.7828          | 0.7194   | 0.6887 |
| 0.0789        | 17.69 | 2600 | 1.6550          | 0.7174   | 0.6987 |
| 0.0789        | 18.37 | 2700 | 1.6418          | 0.7383   | 0.7198 |
| 0.0789        | 19.05 | 2800 | 1.7430          | 0.7453   | 0.7215 |
| 0.0789        | 19.73 | 2900 | 1.8571          | 0.7383   | 0.7122 |
| 0.1           | 20.41 | 3000 | 1.8949          | 0.7323   | 0.6958 |
| 0.1           | 21.09 | 3100 | 1.7967          | 0.7562   | 0.7310 |
| 0.1           | 21.77 | 3200 | 1.7864          | 0.7383   | 0.7149 |
| 0.1           | 22.45 | 3300 | 2.1725          | 0.7254   | 0.6927 |


### 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-pre-finetune-div-2-data-tyqiangz-multilingual-sentiments-mo", "results": []}]}
transformers pytorch xlm-roberta generated_from_trainer dataset:multilingual-sentiments base_model:xlm-roberta-base license:mit endpoints_compatible region:us