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# combine-40-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: 0.2145
- Precision: 0.9368
- Recall: 0.9406
- F1 Weighted: 0.9376
- F1 Macro: 0.8232

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:-----------:|:--------:|
| 0.9949        | 0.1   | 25   | 0.8155          | 0.6773    | 0.5610 | 0.4515      | 0.3048   |
| 0.7063        | 0.21  | 50   | 0.4217          | 0.8504    | 0.8787 | 0.8591      | 0.5998   |
| 0.5545        | 0.31  | 75   | 0.4104          | 0.8674    | 0.9084 | 0.8870      | 0.6198   |
| 0.5285        | 0.41  | 100  | 0.3414          | 0.8648    | 0.9065 | 0.8852      | 0.6182   |
| 0.4649        | 0.51  | 125  | 0.2983          | 0.8755    | 0.9172 | 0.8957      | 0.6257   |
| 0.4305        | 0.62  | 150  | 0.3098          | 0.8763    | 0.9185 | 0.8969      | 0.6266   |
| 0.3806        | 0.72  | 175  | 0.2825          | 0.8780    | 0.9204 | 0.8987      | 0.6280   |
| 0.3606        | 0.82  | 200  | 0.2854          | 0.8804    | 0.9229 | 0.9012      | 0.6299   |
| 0.3633        | 0.93  | 225  | 0.2515          | 0.9321    | 0.9286 | 0.9085      | 0.6509   |
| 0.2473        | 1.03  | 250  | 0.2584          | 0.9277    | 0.9343 | 0.9265      | 0.7655   |
| 0.3906        | 1.13  | 275  | 0.2194          | 0.9264    | 0.9318 | 0.9275      | 0.7923   |
| 0.3153        | 1.23  | 300  | 0.2296          | 0.9427    | 0.9450 | 0.9408      | 0.8312   |
| 0.2881        | 1.34  | 325  | 0.2365          | 0.9276    | 0.9311 | 0.9278      | 0.8088   |
| 0.288         | 1.44  | 350  | 0.2472          | 0.9276    | 0.9299 | 0.9179      | 0.7305   |
| 0.2728        | 1.54  | 375  | 0.2390          | 0.9202    | 0.9248 | 0.9208      | 0.7915   |
| 0.2502        | 1.65  | 400  | 0.2188          | 0.9342    | 0.9375 | 0.9343      | 0.8172   |
| 0.2351        | 1.75  | 425  | 0.2145          | 0.9368    | 0.9406 | 0.9376      | 0.8232   |


### 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-40-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-40-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: 0.2145
* Precision: 0.9368
* Recall: 0.9406
* F1 Weighted: 0.9376
* F1 Macro: 0.8232


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