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# roberta-finetuned-WebClassification-v2-smalllinguaENES

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: 1.0053
- Accuracy: 0.9355
- F1: 0.9355
- Precision: 0.9355
- Recall: 0.9355

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 1.0   | 16   | 2.4058          | 0.1613   | 0.1613 | 0.1613    | 0.1613 |
| No log        | 2.0   | 32   | 2.3931          | 0.0968   | 0.0968 | 0.0968    | 0.0968 |
| No log        | 3.0   | 48   | 1.9594          | 0.4516   | 0.4516 | 0.4516    | 0.4516 |
| No log        | 4.0   | 64   | 1.7428          | 0.6129   | 0.6129 | 0.6129    | 0.6129 |
| No log        | 5.0   | 80   | 1.3781          | 0.8387   | 0.8387 | 0.8387    | 0.8387 |
| No log        | 6.0   | 96   | 1.0053          | 0.9355   | 0.9355 | 0.9355    | 0.9355 |
| No log        | 7.0   | 112  | 0.8489          | 0.8387   | 0.8387 | 0.8387    | 0.8387 |
| No log        | 8.0   | 128  | 0.7135          | 0.8710   | 0.8710 | 0.8710    | 0.8710 |
| No log        | 9.0   | 144  | 0.6700          | 0.8710   | 0.8710 | 0.8710    | 0.8710 |
| No log        | 10.0  | 160  | 0.6511          | 0.9355   | 0.9355 | 0.9355    | 0.9355 |


### Framework versions

- Transformers 4.27.3
- Pytorch 2.0.0+cpu
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "roberta-finetuned-WebClassification-v2-smalllinguaENES", "results": []}]}
transformers pytorch roberta text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

roberta-finetuned-WebClassification-v2-smalllinguaENES
======================================================


This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.0053
* Accuracy: 0.9355
* F1: 0.9355
* Precision: 0.9355
* Recall: 0.9355


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: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.27.3
* Pytorch 2.0.0+cpu
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.27.3
* Pytorch 2.0.0+cpu
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.27.3
* Pytorch 2.0.0+cpu
* Datasets 2.10.1
* Tokenizers 0.13.2