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# combine-60-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: 0.2538
- Precision: 0.8786
- Recall: 0.9210
- F1 Weighted: 0.8993
- F1 Macro: 0.6284

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:-----------:|:--------:|
| 1.12          | 0.09  | 25   | 1.0597          | 0.2586    | 0.5085 | 0.3429      | 0.2247   |
| 0.9016        | 0.18  | 50   | 0.5441          | 0.8258    | 0.8642 | 0.8440      | 0.5895   |
| 0.6163        | 0.27  | 75   | 0.4097          | 0.8713    | 0.9109 | 0.8897      | 0.6215   |
| 0.4973        | 0.36  | 100  | 0.3429          | 0.8726    | 0.9135 | 0.8923      | 0.6234   |
| 0.4666        | 0.46  | 125  | 0.3091          | 0.8774    | 0.9198 | 0.8981      | 0.6277   |
| 0.4458        | 0.55  | 150  | 0.3671          | 0.8788    | 0.8888 | 0.8697      | 0.6153   |
| 0.386         | 0.64  | 175  | 0.2554          | 0.8811    | 0.9229 | 0.9012      | 0.6297   |
| 0.3975        | 0.73  | 200  | 0.2712          | 0.8834    | 0.9255 | 0.9037      | 0.6314   |
| 0.3293        | 0.82  | 225  | 0.2538          | 0.8786    | 0.9210 | 0.8993      | 0.6284   |


### 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": "combine-60-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 

combine-60-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: 0.2538
* Precision: 0.8786
* Recall: 0.9210
* F1 Weighted: 0.8993
* F1 Macro: 0.6284


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