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# synthetic-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: 1.5363
- Precision: 0.8271
- Recall: 0.4694
- F1 Weighted: 0.5676
- F1 Macro: 0.4308

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:-----------:|:--------:|
| 1.1067        | 0.16  | 25   | 1.0890          | 0.4082    | 0.4409 | 0.4087      | 0.2919   |
| 0.8313        | 0.31  | 50   | 1.0063          | 0.8055    | 0.4536 | 0.5326      | 0.4108   |
| 0.5543        | 0.47  | 75   | 1.2653          | 0.7821    | 0.5130 | 0.5959      | 0.4533   |
| 0.561         | 0.62  | 100  | 1.2028          | 0.8212    | 0.6001 | 0.6753      | 0.5171   |
| 0.5141        | 0.78  | 125  | 1.3416          | 0.8536    | 0.4896 | 0.5820      | 0.4459   |
| 0.4595        | 0.94  | 150  | 1.2413          | 0.8543    | 0.6349 | 0.7147      | 0.5427   |
| 0.4647        | 1.09  | 175  | 1.4473          | 0.8470    | 0.5894 | 0.6803      | 0.5160   |
| 0.4351        | 1.25  | 200  | 1.2805          | 0.8418    | 0.6267 | 0.7058      | 0.5377   |
| 0.4012        | 1.41  | 225  | 1.6780          | 0.8222    | 0.4176 | 0.5164      | 0.3948   |
| 0.3711        | 1.56  | 250  | 1.5256          | 0.8483    | 0.5186 | 0.6145      | 0.4644   |
| 0.4064        | 1.72  | 275  | 1.6555          | 0.8420    | 0.4593 | 0.5710      | 0.4323   |
| 0.3905        | 1.88  | 300  | 1.5819          | 0.8442    | 0.4024 | 0.4908      | 0.3764   |
| 0.3487        | 2.03  | 325  | 1.5363          | 0.8271    | 0.4694 | 0.5676      | 0.4308   |


### 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-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-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: 1.5363
* Precision: 0.8271
* Recall: 0.4694
* F1 Weighted: 0.5676
* F1 Macro: 0.4308


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