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# roberta-finetuned-WebClassification-v2-smalllinguaESv2

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: 1.3862
- Accuracy: 0.6909
- F1: 0.6909
- Precision: 0.6909
- Recall: 0.6909

## 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   | 28   | 2.9841          | 0.2      | 0.2000 | 0.2       | 0.2    |
| No log        | 2.0   | 56   | 2.8109          | 0.1636   | 0.1636 | 0.1636    | 0.1636 |
| No log        | 3.0   | 84   | 2.5334          | 0.3455   | 0.3455 | 0.3455    | 0.3455 |
| No log        | 4.0   | 112  | 2.1164          | 0.5273   | 0.5273 | 0.5273    | 0.5273 |
| No log        | 5.0   | 140  | 1.9152          | 0.5818   | 0.5818 | 0.5818    | 0.5818 |
| No log        | 6.0   | 168  | 1.6678          | 0.6182   | 0.6182 | 0.6182    | 0.6182 |
| No log        | 7.0   | 196  | 1.5647          | 0.6545   | 0.6545 | 0.6545    | 0.6545 |
| No log        | 8.0   | 224  | 1.4473          | 0.6727   | 0.6727 | 0.6727    | 0.6727 |
| No log        | 9.0   | 252  | 1.3862          | 0.6909   | 0.6909 | 0.6909    | 0.6909 |
| No log        | 10.0  | 280  | 1.3647          | 0.6909   | 0.6909 | 0.6909    | 0.6909 |


### Framework versions

- Transformers 4.31.0.dev0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "roberta-finetuned-WebClassification-v2-smalllinguaESv2", "results": []}]}
transformers pytorch tensorboard roberta text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

roberta-finetuned-WebClassification-v2-smalllinguaESv2
======================================================


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: 1.3862
* Accuracy: 0.6909
* F1: 0.6909
* Precision: 0.6909
* Recall: 0.6909


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 4.31.0.dev0
* Pytorch 2.0.0
* Datasets 2.1.0
* Tokenizers 0.13.3



### 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 4.31.0.dev0
* Pytorch 2.0.0
* Datasets 2.1.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #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 4.31.0.dev0
* Pytorch 2.0.0
* Datasets 2.1.0
* Tokenizers 0.13.3