
<!-- 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-pre-finetune-silver-div-2-data-tyqiangz-multilingual-sentiments

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: 9.7814
- Accuracy: 0.6389
- F1: 0.6411

## 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        | 1.72  | 100  | 8.3730          | 0.5957   | 0.5987 |
| No log        | 3.45  | 200  | 7.6900          | 0.5957   | 0.5918 |
| No log        | 5.17  | 300  | 8.7731          | 0.5833   | 0.5881 |
| No log        | 6.9   | 400  | 8.3024          | 0.6019   | 0.6041 |
| 6.57          | 8.62  | 500  | 9.4341          | 0.6019   | 0.6028 |
| 6.57          | 10.34 | 600  | 13.0140         | 0.5340   | 0.5234 |
| 6.57          | 12.07 | 700  | 8.9061          | 0.5895   | 0.5876 |
| 6.57          | 13.79 | 800  | 10.8173         | 0.5648   | 0.5672 |
| 6.57          | 15.52 | 900  | 9.9649          | 0.6204   | 0.6125 |
| 1.6429        | 17.24 | 1000 | 10.6104         | 0.5340   | 0.5370 |
| 1.6429        | 18.97 | 1100 | 13.4879         | 0.5710   | 0.5463 |
| 1.6429        | 20.69 | 1200 | 9.3471          | 0.6173   | 0.6071 |
| 1.6429        | 22.41 | 1300 | 9.0244          | 0.5988   | 0.6010 |
| 1.6429        | 24.14 | 1400 | 8.9435          | 0.5833   | 0.5817 |
| 0.9221        | 25.86 | 1500 | 9.3570          | 0.5988   | 0.6008 |
| 0.9221        | 27.59 | 1600 | 9.3423          | 0.5926   | 0.5970 |
| 0.9221        | 29.31 | 1700 | 9.0833          | 0.6019   | 0.6018 |
| 0.9221        | 31.03 | 1800 | 8.3567          | 0.6173   | 0.6193 |
| 0.9221        | 32.76 | 1900 | 9.2510          | 0.6111   | 0.6104 |
| 0.5179        | 34.48 | 2000 | 9.7708          | 0.6142   | 0.6133 |
| 0.5179        | 36.21 | 2100 | 10.7333         | 0.5772   | 0.5814 |
| 0.5179        | 37.93 | 2200 | 10.7140         | 0.6049   | 0.6068 |
| 0.5179        | 39.66 | 2300 | 10.9694         | 0.5895   | 0.5879 |
| 0.5179        | 41.38 | 2400 | 10.1105         | 0.6019   | 0.6020 |
| 0.3244        | 43.1  | 2500 | 9.4198          | 0.6389   | 0.6389 |
| 0.3244        | 44.83 | 2600 | 11.1285         | 0.5864   | 0.5890 |
| 0.3244        | 46.55 | 2700 | 10.4855         | 0.6049   | 0.6005 |
| 0.3244        | 48.28 | 2800 | 9.4305          | 0.6142   | 0.6142 |
| 0.3244        | 50.0  | 2900 | 9.7035          | 0.6235   | 0.6217 |
| 0.2361        | 51.72 | 3000 | 8.9405          | 0.6296   | 0.6297 |
| 0.2361        | 53.45 | 3100 | 10.5672         | 0.6265   | 0.6005 |
| 0.2361        | 55.17 | 3200 | 9.3846          | 0.6265   | 0.6257 |
| 0.2361        | 56.9  | 3300 | 10.5135         | 0.6142   | 0.6133 |
| 0.2361        | 58.62 | 3400 | 10.6095         | 0.5957   | 0.6011 |
| 0.1879        | 60.34 | 3500 | 9.5250          | 0.6574   | 0.6586 |
| 0.1879        | 62.07 | 3600 | 9.6180          | 0.6111   | 0.6137 |
| 0.1879        | 63.79 | 3700 | 10.0628         | 0.5895   | 0.5945 |
| 0.1879        | 65.52 | 3800 | 9.7125          | 0.5895   | 0.5936 |
| 0.1879        | 67.24 | 3900 | 9.3930          | 0.6296   | 0.6213 |
| 0.1583        | 68.97 | 4000 | 9.3178          | 0.6173   | 0.6216 |
| 0.1583        | 70.69 | 4100 | 8.9921          | 0.6019   | 0.6046 |
| 0.1583        | 72.41 | 4200 | 10.3695         | 0.6049   | 0.6008 |
| 0.1583        | 74.14 | 4300 | 8.7056          | 0.6296   | 0.6290 |
| 0.1583        | 75.86 | 4400 | 9.2988          | 0.5957   | 0.6000 |
| 0.145         | 77.59 | 4500 | 8.6886          | 0.6420   | 0.6431 |
| 0.145         | 79.31 | 4600 | 9.0239          | 0.5957   | 0.5960 |
| 0.145         | 81.03 | 4700 | 9.5018          | 0.6235   | 0.6201 |
| 0.145         | 82.76 | 4800 | 8.3157          | 0.6235   | 0.6237 |
| 0.145         | 84.48 | 4900 | 8.9491          | 0.6049   | 0.6051 |
| 0.1299        | 86.21 | 5000 | 9.7814          | 0.6389   | 0.6411 |


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