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# ES-ENG-xlm-roberta-sentiment

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on a Custom dataset.

The best model (stopped after 20 epochs) achieves the following results on the evaluation set:
- Loss: 0.7743
- Accuracy: 0.6702
- F1: 0.6672
- Precision: 0.6664
- Recall: 0.6702


## Intended uses & limitations

Note that commercial use with this model is prohibited. 


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-06
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.1099        | 1.0   | 208  | 1.0718          | 0.3968   | 0.3851 | 0.4857    | 0.3968 |
| 1.0057        | 2.0   | 416  | 0.8926          | 0.5492   | 0.5080 | 0.5639    | 0.5492 |
| 0.8988        | 3.0   | 624  | 0.8384          | 0.5883   | 0.5792 | 0.5789    | 0.5883 |
| 0.8606        | 4.0   | 832  | 0.8209          | 0.6168   | 0.6086 | 0.6086    | 0.6168 |
| 0.8338        | 5.0   | 1040 | 0.8006          | 0.6120   | 0.6068 | 0.6046    | 0.6120 |
| 0.8081        | 6.0   | 1248 | 0.8074          | 0.6026   | 0.5935 | 0.5966    | 0.6026 |
| 0.7872        | 7.0   | 1456 | 0.7786          | 0.6194   | 0.6149 | 0.6127    | 0.6194 |
| 0.7624        | 8.0   | 1664 | 0.7783          | 0.6379   | 0.6277 | 0.6342    | 0.6379 |
| 0.7446        | 9.0   | 1872 | 0.7643          | 0.6366   | 0.6287 | 0.6314    | 0.6366 |
| 0.7274        | 10.0  | 2080 | 0.7846          | 0.6395   | 0.6297 | 0.6351    | 0.6395 |
| 0.7116        | 11.0  | 2288 | 0.7465          | 0.6495   | 0.6425 | 0.6462    | 0.6495 |
| 0.6998        | 12.0  | 2496 | 0.7599          | 0.6537   | 0.6474 | 0.6494    | 0.6537 |
| 0.6852        | 13.0  | 2704 | 0.7651          | 0.6515   | 0.6443 | 0.6465    | 0.6515 |
| 0.6726        | 14.0  | 2912 | 0.7571          | 0.6576   | 0.6536 | 0.6530    | 0.6576 |
| 0.6665        | 15.0  | 3120 | 0.7597          | 0.6557   | 0.6506 | 0.6514    | 0.6557 |
| 0.6541        | 16.0  | 3328 | 0.7590          | 0.6615   | 0.6584 | 0.6576    | 0.6615 |
| 0.6513        | 17.0  | 3536 | 0.7617          | 0.6599   | 0.6544 | 0.6555    | 0.6599 |
| 0.6392        | 18.0  | 3744 | 0.7740          | 0.6628   | 0.6585 | 0.6582    | 0.6628 |
| 0.6369        | 19.0  | 3952 | 0.7666          | 0.6631   | 0.6588 | 0.6585    | 0.6631 |
| 0.6268        | 20.0  | 4160 | 0.7743          | 0.6702   | 0.6672 | 0.6664    | 0.6702 |
| 0.62          | 21.0  | 4368 | 0.7712          | 0.6680   | 0.6638 | 0.6638    | 0.6680 |
| 0.619         | 22.0  | 4576 | 0.7720          | 0.6689   | 0.6656 | 0.6649    | 0.6689 |
| 0.6074        | 23.0  | 4784 | 0.7729          | 0.6663   | 0.6630 | 0.6621    | 0.6663 |


### Framework versions

- Transformers 4.29.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
{"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "ES-ENG-xlm-roberta-sentiment", "results": []}]}
transformers pytorch xlm-roberta text-classification generated_from_trainer license:cc-by-nc-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #xlm-roberta #text-classification #generated_from_trainer #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us 

ES-ENG-xlm-roberta-sentiment
============================


This model is a fine-tuned version of xlm-roberta-base on a Custom dataset.


The best model (stopped after 20 epochs) achieves the following results on the evaluation set:


* Loss: 0.7743
* Accuracy: 0.6702
* F1: 0.6672
* Precision: 0.6664
* Recall: 0.6702


Intended uses & limitations
---------------------------


Note that commercial use with this model is prohibited.


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-06
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30


### Training results



### Framework versions


* Transformers 4.29.1
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-06
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.29.1
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #xlm-roberta #text-classification #generated_from_trainer #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-06
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.29.1
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3