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# synthetic-70-vsfc-xlm-r

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: 1.7726
- Precision: 0.8498
- Recall: 0.4018
- F1 Weighted: 0.5020
- F1 Macro: 0.3865

## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 Weighted | F1 Macro |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:-----------:|:--------:|
| 1.1007        | 0.22  | 25   | 1.1227          | 0.0021    | 0.0461 | 0.0041      | 0.0294   |
| 0.8388        | 0.45  | 50   | 0.7289          | 0.8086    | 0.8105 | 0.8075      | 0.6172   |
| 0.5915        | 0.67  | 75   | 1.0029          | 0.8601    | 0.5155 | 0.6026      | 0.4582   |
| 0.4876        | 0.89  | 100  | 1.5117          | 0.8650    | 0.4820 | 0.5787      | 0.4426   |
| 0.3964        | 1.12  | 125  | 1.3913          | 0.8461    | 0.3973 | 0.4908      | 0.3829   |
| 0.4464        | 1.34  | 150  | 1.3103          | 0.8332    | 0.5130 | 0.5554      | 0.4201   |
| 0.485         | 1.56  | 175  | 1.5958          | 0.8831    | 0.4277 | 0.5336      | 0.4108   |
| 0.4471        | 1.79  | 200  | 1.5570          | 0.8774    | 0.4713 | 0.5527      | 0.4252   |
| 0.3867        | 2.01  | 225  | 1.7726          | 0.8498    | 0.4018 | 0.5020      | 0.3865   |


### Framework versions

- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall"], "model-index": [{"name": "synthetic-70-vsfc-xlm-r", "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 

synthetic-70-vsfc-xlm-r
=======================


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: 1.7726
* Precision: 0.8498
* Recall: 0.4018
* F1 Weighted: 0.5020
* F1 Macro: 0.3865


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: 5e-05
* train\_batch\_size: 64
* eval\_batch\_size: 128
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 5


### Training results



### Framework versions


* Transformers 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 64
* eval\_batch\_size: 128
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
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: 5e-05
* train\_batch\_size: 64
* eval\_batch\_size: 128
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2