
<!-- 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. -->

# xlm-roberta-base-finetuned-semantics

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0033
- F1: 1.0

## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.7438        | 1.0   | 10   | 0.3942          | 0.7160 |
| 0.3045        | 2.0   | 20   | 0.1455          | 0.8608 |
| 0.1072        | 3.0   | 30   | 0.0673          | 0.9114 |
| 0.0647        | 4.0   | 40   | 0.0389          | 0.9620 |
| 0.0477        | 5.0   | 50   | 0.0212          | 0.9620 |
| 0.0164        | 6.0   | 60   | 0.0258          | 0.9744 |
| 0.0215        | 7.0   | 70   | 0.0303          | 0.9744 |
| 0.022         | 8.0   | 80   | 0.0096          | 1.0    |
| 0.0057        | 9.0   | 90   | 0.0104          | 1.0    |
| 0.0069        | 10.0  | 100  | 0.0101          | 1.0    |
| 0.0028        | 11.0  | 110  | 0.0085          | 1.0    |
| 0.0109        | 12.0  | 120  | 0.0128          | 0.9744 |
| 0.0048        | 13.0  | 130  | 0.0136          | 0.9744 |
| 0.0045        | 14.0  | 140  | 0.0145          | 0.9744 |
| 0.003         | 15.0  | 150  | 0.0142          | 0.9744 |
| 0.0013        | 16.0  | 160  | 0.0140          | 0.9744 |
| 0.0025        | 17.0  | 170  | 0.0134          | 0.9744 |
| 0.001         | 18.0  | 180  | 0.0110          | 0.9744 |
| 0.001         | 19.0  | 190  | 0.0085          | 0.9744 |
| 0.0007        | 20.0  | 200  | 0.0038          | 1.0    |
| 0.0007        | 21.0  | 210  | 0.0008          | 1.0    |
| 0.0043        | 22.0  | 220  | 0.0009          | 1.0    |
| 0.0045        | 23.0  | 230  | 0.0011          | 1.0    |
| 0.0008        | 24.0  | 240  | 0.0012          | 1.0    |
| 0.0009        | 25.0  | 250  | 0.0013          | 1.0    |
| 0.0006        | 26.0  | 260  | 0.0012          | 1.0    |
| 0.0006        | 27.0  | 270  | 0.0011          | 1.0    |
| 0.0013        | 28.0  | 280  | 0.0012          | 1.0    |
| 0.0006        | 29.0  | 290  | 0.0013          | 1.0    |
| 0.0017        | 30.0  | 300  | 0.0004          | 1.0    |
| 0.0019        | 31.0  | 310  | 0.0005          | 1.0    |
| 0.0137        | 32.0  | 320  | 0.0042          | 1.0    |
| 0.0023        | 33.0  | 330  | 0.0280          | 0.9744 |
| 0.0099        | 34.0  | 340  | 0.0327          | 0.9744 |
| 0.0085        | 35.0  | 350  | 0.0196          | 0.9744 |
| 0.001         | 36.0  | 360  | 0.0043          | 1.0    |
| 0.0014        | 37.0  | 370  | 0.0006          | 1.0    |
| 0.0005        | 38.0  | 380  | 0.0005          | 1.0    |
| 0.0016        | 39.0  | 390  | 0.0004          | 1.0    |
| 0.0005        | 40.0  | 400  | 0.0006          | 1.0    |
| 0.009         | 41.0  | 410  | 0.0007          | 1.0    |
| 0.0008        | 42.0  | 420  | 0.0006          | 1.0    |
| 0.0005        | 43.0  | 430  | 0.0006          | 1.0    |
| 0.0004        | 44.0  | 440  | 0.0006          | 1.0    |
| 0.0004        | 45.0  | 450  | 0.0006          | 1.0    |
| 0.001         | 46.0  | 460  | 0.0004          | 1.0    |
| 0.0006        | 47.0  | 470  | 0.0004          | 1.0    |
| 0.0006        | 48.0  | 480  | 0.0012          | 1.0    |
| 0.0003        | 49.0  | 490  | 0.0022          | 1.0    |
| 0.0004        | 50.0  | 500  | 0.0025          | 1.0    |
| 0.0003        | 51.0  | 510  | 0.0025          | 1.0    |
| 0.0003        | 52.0  | 520  | 0.0025          | 1.0    |
| 0.0014        | 53.0  | 530  | 0.0026          | 1.0    |
| 0.0003        | 54.0  | 540  | 0.0033          | 1.0    |
| 0.0003        | 55.0  | 550  | 0.0034          | 1.0    |
| 0.0029        | 56.0  | 560  | 0.0033          | 1.0    |
| 0.0022        | 57.0  | 570  | 0.0032          | 1.0    |
| 0.0024        | 58.0  | 580  | 0.0032          | 1.0    |
| 0.0003        | 59.0  | 590  | 0.0030          | 1.0    |
| 0.0006        | 60.0  | 600  | 0.0068          | 0.9744 |
| 0.0003        | 61.0  | 610  | 0.0093          | 0.9744 |
| 0.0002        | 62.0  | 620  | 0.0098          | 0.9744 |
| 0.0003        | 63.0  | 630  | 0.0096          | 0.9744 |
| 0.0002        | 64.0  | 640  | 0.0090          | 0.9744 |
| 0.0003        | 65.0  | 650  | 0.0073          | 0.9744 |
| 0.0002        | 66.0  | 660  | 0.0058          | 1.0    |
| 0.0079        | 67.0  | 670  | 0.0024          | 1.0    |
| 0.0005        | 68.0  | 680  | 0.0006          | 1.0    |
| 0.0028        | 69.0  | 690  | 0.0004          | 1.0    |
| 0.0077        | 70.0  | 700  | 0.0005          | 1.0    |
| 0.0004        | 71.0  | 710  | 0.0004          | 1.0    |
| 0.0078        | 72.0  | 720  | 0.0002          | 1.0    |
| 0.0047        | 73.0  | 730  | 0.0029          | 1.0    |
| 0.0004        | 74.0  | 740  | 0.0067          | 1.0    |
| 0.0004        | 75.0  | 750  | 0.0077          | 1.0    |
| 0.0003        | 76.0  | 760  | 0.0078          | 1.0    |
| 0.0021        | 77.0  | 770  | 0.0075          | 1.0    |
| 0.0003        | 78.0  | 780  | 0.0071          | 1.0    |
| 0.0019        | 79.0  | 790  | 0.0066          | 1.0    |
| 0.0003        | 80.0  | 800  | 0.0062          | 1.0    |
| 0.0003        | 81.0  | 810  | 0.0057          | 1.0    |
| 0.0034        | 82.0  | 820  | 0.0052          | 1.0    |
| 0.0002        | 83.0  | 830  | 0.0048          | 1.0    |
| 0.0003        | 84.0  | 840  | 0.0046          | 1.0    |
| 0.0002        | 85.0  | 850  | 0.0044          | 1.0    |
| 0.0002        | 86.0  | 860  | 0.0043          | 1.0    |
| 0.0002        | 87.0  | 870  | 0.0041          | 1.0    |
| 0.0053        | 88.0  | 880  | 0.0037          | 1.0    |
| 0.0034        | 89.0  | 890  | 0.0036          | 1.0    |
| 0.0002        | 90.0  | 900  | 0.0036          | 1.0    |
| 0.0002        | 91.0  | 910  | 0.0035          | 1.0    |
| 0.0002        | 92.0  | 920  | 0.0035          | 1.0    |
| 0.0002        | 93.0  | 930  | 0.0035          | 1.0    |
| 0.0002        | 94.0  | 940  | 0.0034          | 1.0    |
| 0.0002        | 95.0  | 950  | 0.0034          | 1.0    |
| 0.0023        | 96.0  | 960  | 0.0034          | 1.0    |
| 0.0002        | 97.0  | 970  | 0.0034          | 1.0    |
| 0.0002        | 98.0  | 980  | 0.0034          | 1.0    |
| 0.0021        | 99.0  | 990  | 0.0033          | 1.0    |
| 0.0003        | 100.0 | 1000 | 0.0033          | 1.0    |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.0.1
- Datasets 2.14.6
- Tokenizers 0.14.1

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-finetuned-semantics", "results": []}]}
transformers pytorch xlm-roberta token-classification generated_from_trainer base_model:xlm-roberta-base license:mit autotrain_compatible endpoints_compatible region:us