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# synthetic-50-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: 1.4091
- Precision: 0.8556
- Recall: 0.5243
- F1 Weighted: 0.6121
- F1 Macro: 0.4655

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:-----------:|:--------:|
| 1.0942        | 0.31  | 25   | 1.0558          | 0.8673    | 0.3816 | 0.3914      | 0.2948   |
| 0.9353        | 0.62  | 50   | 1.0523          | 0.8262    | 0.7448 | 0.7727      | 0.5946   |
| 0.6801        | 0.94  | 75   | 1.5973          | 0.8845    | 0.3664 | 0.4515      | 0.3550   |
| 0.5368        | 1.25  | 100  | 1.2531          | 0.8369    | 0.5755 | 0.6656      | 0.5041   |
| 0.4458        | 1.56  | 125  | 1.4147          | 0.8289    | 0.4485 | 0.5348      | 0.4102   |
| 0.4611        | 1.88  | 150  | 1.4477          | 0.8686    | 0.4649 | 0.5568      | 0.4276   |
| 0.3957        | 2.19  | 175  | 1.7033          | 0.8750    | 0.4637 | 0.5633      | 0.4317   |
| 0.4183        | 2.5   | 200  | 1.6080          | 0.8555    | 0.4479 | 0.5540      | 0.4217   |
| 0.3482        | 2.81  | 225  | 1.4091          | 0.8556    | 0.5243 | 0.6121      | 0.4655   |


### 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-50-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-50-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: 1.4091
* Precision: 0.8556
* Recall: 0.5243
* F1 Weighted: 0.6121
* F1 Macro: 0.4655


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
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