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# uit-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: 0.2435
- Precision: 0.9352
- Recall: 0.9381
- F1 Weighted: 0.9359
- F1 Macro: 0.8355

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:-----------:|:--------:|
| 0.9917        | 0.14  | 25   | 0.8298          | 0.7568    | 0.7473 | 0.7280      | 0.5092   |
| 0.6922        | 0.28  | 50   | 0.4733          | 0.8513    | 0.8901 | 0.8694      | 0.6071   |
| 0.3939        | 0.42  | 75   | 0.3388          | 0.8695    | 0.9116 | 0.8900      | 0.6217   |
| 0.341         | 0.56  | 100  | 0.3277          | 0.8705    | 0.9059 | 0.8854      | 0.6180   |
| 0.25          | 0.7   | 125  | 0.2472          | 0.8803    | 0.9223 | 0.9007      | 0.6290   |
| 0.3081        | 0.84  | 150  | 0.2520          | 0.8798    | 0.9185 | 0.8974      | 0.6265   |
| 0.2782        | 0.98  | 175  | 0.2321          | 0.8823    | 0.9223 | 0.9010      | 0.6290   |
| 0.2496        | 1.12  | 200  | 0.2476          | 0.9187    | 0.9191 | 0.9177      | 0.7688   |
| 0.2651        | 1.26  | 225  | 0.2530          | 0.9251    | 0.9305 | 0.9258      | 0.7883   |
| 0.2531        | 1.4   | 250  | 0.2207          | 0.9337    | 0.9381 | 0.9339      | 0.8097   |
| 0.2863        | 1.54  | 275  | 0.2243          | 0.9243    | 0.9280 | 0.9259      | 0.7783   |
| 0.2469        | 1.68  | 300  | 0.2437          | 0.9350    | 0.9381 | 0.9314      | 0.7926   |
| 0.2218        | 1.82  | 325  | 0.2663          | 0.9243    | 0.9261 | 0.9240      | 0.8137   |
| 0.2299        | 1.96  | 350  | 0.2365          | 0.9368    | 0.9400 | 0.9377      | 0.8321   |
| 0.1664        | 2.09  | 375  | 0.2295          | 0.9375    | 0.9413 | 0.9377      | 0.8238   |
| 0.1682        | 2.23  | 400  | 0.2213          | 0.9386    | 0.9400 | 0.9392      | 0.8381   |
| 0.1803        | 2.37  | 425  | 0.2207          | 0.9347    | 0.9368 | 0.9355      | 0.8320   |
| 0.1509        | 2.51  | 450  | 0.2197          | 0.9388    | 0.9419 | 0.9371      | 0.8135   |
| 0.1338        | 2.65  | 475  | 0.2480          | 0.9349    | 0.9375 | 0.9303      | 0.7886   |
| 0.1525        | 2.79  | 500  | 0.2291          | 0.9405    | 0.9438 | 0.9410      | 0.8352   |
| 0.1427        | 2.93  | 525  | 0.2435          | 0.9352    | 0.9381 | 0.9359      | 0.8355   |


### 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": "uit-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 

uit-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: 0.2435
* Precision: 0.9352
* Recall: 0.9381
* F1 Weighted: 0.9359
* F1 Macro: 0.8355


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