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# combine-20-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: 0.2322
- Precision: 0.9414
- Recall: 0.9438
- F1 Weighted: 0.9409
- F1 Macro: 0.8449

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:-----------:|:--------:|
| 0.9866        | 0.12  | 25   | 0.8014          | 0.7299    | 0.7088 | 0.6862      | 0.4808   |
| 0.7581        | 0.24  | 50   | 0.5130          | 0.8573    | 0.8920 | 0.8717      | 0.6086   |
| 0.5523        | 0.36  | 75   | 0.4340          | 0.8637    | 0.9021 | 0.8812      | 0.6154   |
| 0.4144        | 0.47  | 100  | 0.3586          | 0.8664    | 0.9052 | 0.8841      | 0.6176   |
| 0.4314        | 0.59  | 125  | 0.2651          | 0.8946    | 0.9172 | 0.9009      | 0.6580   |
| 0.3391        | 0.71  | 150  | 0.2658          | 0.9078    | 0.9204 | 0.9116      | 0.7174   |
| 0.3441        | 0.83  | 175  | 0.2518          | 0.9198    | 0.9286 | 0.9190      | 0.7342   |
| 0.3624        | 0.95  | 200  | 0.2484          | 0.9273    | 0.9318 | 0.9173      | 0.7057   |
| 0.2703        | 1.07  | 225  | 0.2388          | 0.9348    | 0.9356 | 0.9261      | 0.7638   |
| 0.2913        | 1.18  | 250  | 0.2496          | 0.9281    | 0.9311 | 0.9209      | 0.7485   |
| 0.3268        | 1.3   | 275  | 0.2504          | 0.9317    | 0.9349 | 0.9279      | 0.7856   |
| 0.2692        | 1.42  | 300  | 0.2163          | 0.9277    | 0.9305 | 0.9239      | 0.7874   |
| 0.2913        | 1.54  | 325  | 0.2264          | 0.9270    | 0.9311 | 0.9256      | 0.7919   |
| 0.2416        | 1.66  | 350  | 0.2304          | 0.9371    | 0.9387 | 0.9333      | 0.8128   |
| 0.2158        | 1.78  | 375  | 0.2419          | 0.9359    | 0.9381 | 0.9338      | 0.8206   |
| 0.2593        | 1.9   | 400  | 0.2269          | 0.9382    | 0.9419 | 0.9370      | 0.8136   |
| 0.2331        | 2.01  | 425  | 0.2534          | 0.9364    | 0.9387 | 0.9341      | 0.8172   |
| 0.2067        | 2.13  | 450  | 0.2199          | 0.9404    | 0.9438 | 0.9407      | 0.8330   |
| 0.2102        | 2.25  | 475  | 0.2429          | 0.9288    | 0.9305 | 0.9270      | 0.8193   |
| 0.1696        | 2.37  | 500  | 0.2271          | 0.9378    | 0.9406 | 0.9382      | 0.8353   |
| 0.2598        | 2.49  | 525  | 0.2175          | 0.9360    | 0.9394 | 0.9370      | 0.8256   |
| 0.243         | 2.61  | 550  | 0.1947          | 0.9457    | 0.9482 | 0.9458      | 0.8520   |
| 0.1944        | 2.73  | 575  | 0.2052          | 0.9419    | 0.9450 | 0.9419      | 0.8354   |
| 0.1839        | 2.84  | 600  | 0.2186          | 0.9405    | 0.9425 | 0.9389      | 0.8358   |
| 0.1829        | 2.96  | 625  | 0.1944          | 0.9455    | 0.9476 | 0.9456      | 0.8583   |
| 0.1705        | 3.08  | 650  | 0.2410          | 0.9355    | 0.9387 | 0.9348      | 0.8223   |
| 0.1258        | 3.2   | 675  | 0.2225          | 0.9381    | 0.9400 | 0.9386      | 0.8475   |
| 0.11          | 3.32  | 700  | 0.2311          | 0.9410    | 0.9438 | 0.9417      | 0.8431   |
| 0.1619        | 3.44  | 725  | 0.2129          | 0.9411    | 0.9431 | 0.9419      | 0.8470   |
| 0.1698        | 3.55  | 750  | 0.2254          | 0.9388    | 0.9413 | 0.9395      | 0.8419   |
| 0.1495        | 3.67  | 775  | 0.2185          | 0.9408    | 0.9438 | 0.9403      | 0.8337   |
| 0.0989        | 3.79  | 800  | 0.2322          | 0.9414    | 0.9438 | 0.9409      | 0.8449   |


### 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-20-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-20-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: 0.2322
* Precision: 0.9414
* Recall: 0.9438
* F1 Weighted: 0.9409
* F1 Macro: 0.8449


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