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# roberta-finetuned-WebClassification-v2-smalllinguaENv2

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: 1.7384
- Accuracy: 0.55
- F1: 0.55
- Precision: 0.55
- Recall: 0.55

## 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   | 20   | 2.9187          | 0.075    | 0.075  | 0.075     | 0.075  |
| No log        | 2.0   | 40   | 2.8030          | 0.15     | 0.15   | 0.15      | 0.15   |
| No log        | 3.0   | 60   | 2.6273          | 0.325    | 0.325  | 0.325     | 0.325  |
| No log        | 4.0   | 80   | 2.3371          | 0.45     | 0.45   | 0.45      | 0.45   |
| No log        | 5.0   | 100  | 2.1233          | 0.425    | 0.425  | 0.425     | 0.425  |
| No log        | 6.0   | 120  | 1.9737          | 0.525    | 0.525  | 0.525     | 0.525  |
| No log        | 7.0   | 140  | 1.8962          | 0.475    | 0.4750 | 0.475     | 0.475  |
| No log        | 8.0   | 160  | 1.8013          | 0.525    | 0.525  | 0.525     | 0.525  |
| No log        | 9.0   | 180  | 1.7384          | 0.55     | 0.55   | 0.55      | 0.55   |
| No log        | 10.0  | 200  | 1.7237          | 0.55     | 0.55   | 0.55      | 0.55   |


### 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-smalllinguaENv2", "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-smalllinguaENv2
======================================================


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: 1.7384
* Accuracy: 0.55
* F1: 0.55
* Precision: 0.55
* Recall: 0.55


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