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# xlm-roberta-base-finetuned-combined-DS

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0232
- Accuracy: 0.6362
- Precision: 0.6193
- Recall: 0.6204
- F1: 0.6160

## 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: 4.1187640010910775e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.0408        | 1.0   | 711  | 1.0206          | 0.5723   | 0.5597    | 0.5122 | 0.4897 |
| 0.9224        | 2.0   | 1422 | 0.9092          | 0.5695   | 0.5745    | 0.5610 | 0.5572 |
| 0.8395        | 3.0   | 2133 | 0.8878          | 0.6088   | 0.6083    | 0.6071 | 0.5981 |
| 0.7418        | 3.99  | 2844 | 0.8828          | 0.6088   | 0.6009    | 0.6068 | 0.5936 |
| 0.6484        | 4.99  | 3555 | 0.9636          | 0.6355   | 0.6235    | 0.6252 | 0.6184 |
| 0.5644        | 5.99  | 4266 | 1.0232          | 0.6362   | 0.6193    | 0.6204 | 0.6160 |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1

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

xlm-roberta-base-finetuned-combined-DS
======================================


This model is a fine-tuned version of xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 1.0232
* Accuracy: 0.6362
* Precision: 0.6193
* Recall: 0.6204
* F1: 0.6160


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: 4.1187640010910775e-05
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 43
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6


### Training results



### Framework versions


* Transformers 4.20.1
* Pytorch 1.10.1+cu111
* Datasets 2.3.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 4.1187640010910775e-05
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 43
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.10.1+cu111
* Datasets 2.3.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #xlm-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: 4.1187640010910775e-05
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 43
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.10.1+cu111
* Datasets 2.3.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #xlm-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: 4.1187640010910775e-05
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 43
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6### Training results### Framework versions


* Transformers 4.20.1
* Pytorch 1.10.1+cu111
* Datasets 2.3.2
* Tokenizers 0.12.1