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# combine-80-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: 0.2271
- Precision: 0.9369
- Recall: 0.9394
- F1 Weighted: 0.9350
- F1 Macro: 0.8215

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:-----------:|:--------:|
| 1.058         | 0.08  | 25   | 0.8369          | 0.6858    | 0.6867 | 0.6569      | 0.4560   |
| 0.9176        | 0.16  | 50   | 0.5039          | 0.8362    | 0.8743 | 0.8541      | 0.5962   |
| 0.5966        | 0.25  | 75   | 0.4431          | 0.8507    | 0.8806 | 0.8609      | 0.6014   |
| 0.6479        | 0.33  | 100  | 0.5620          | 0.8416    | 0.8490 | 0.8449      | 0.6032   |
| 0.5151        | 0.41  | 125  | 0.3634          | 0.8689    | 0.9097 | 0.8883      | 0.6209   |
| 0.4465        | 0.49  | 150  | 0.3030          | 0.9070    | 0.9059 | 0.9063      | 0.7148   |
| 0.479         | 0.57  | 175  | 0.3010          | 0.9150    | 0.9242 | 0.9121      | 0.7101   |
| 0.3566        | 0.65  | 200  | 0.2962          | 0.9060    | 0.9236 | 0.9032      | 0.6388   |
| 0.3922        | 0.74  | 225  | 0.3038          | 0.8724    | 0.9084 | 0.8877      | 0.6198   |
| 0.3604        | 0.82  | 250  | 0.2561          | 0.9146    | 0.9255 | 0.9076      | 0.6647   |
| 0.3479        | 0.9   | 275  | 0.2598          | 0.9053    | 0.9071 | 0.8963      | 0.7053   |
| 0.3567        | 0.98  | 300  | 0.2927          | 0.9088    | 0.9191 | 0.8996      | 0.6444   |
| 0.3138        | 1.06  | 325  | 0.2448          | 0.9311    | 0.9337 | 0.9206      | 0.7201   |
| 0.3014        | 1.14  | 350  | 0.3007          | 0.9202    | 0.9261 | 0.9104      | 0.6875   |
| 0.3404        | 1.23  | 375  | 0.2653          | 0.9203    | 0.9248 | 0.9138      | 0.7314   |
| 0.2914        | 1.31  | 400  | 0.2585          | 0.9315    | 0.9330 | 0.9178      | 0.6999   |
| 0.3532        | 1.39  | 425  | 0.2592          | 0.9226    | 0.9236 | 0.9142      | 0.7486   |
| 0.289         | 1.47  | 450  | 0.2149          | 0.9389    | 0.9419 | 0.9376      | 0.8217   |
| 0.2433        | 1.55  | 475  | 0.2335          | 0.9303    | 0.9343 | 0.9311      | 0.8148   |
| 0.2934        | 1.63  | 500  | 0.2241          | 0.9344    | 0.9318 | 0.9329      | 0.8148   |
| 0.2717        | 1.72  | 525  | 0.2238          | 0.9342    | 0.9381 | 0.9337      | 0.8123   |
| 0.2696        | 1.8   | 550  | 0.2396          | 0.9347    | 0.9381 | 0.9351      | 0.8277   |
| 0.2654        | 1.88  | 575  | 0.2508          | 0.9335    | 0.9356 | 0.9267      | 0.7679   |
| 0.2687        | 1.96  | 600  | 0.2119          | 0.9344    | 0.9381 | 0.9350      | 0.8228   |
| 0.304         | 2.04  | 625  | 0.2453          | 0.9290    | 0.9311 | 0.9237      | 0.7795   |
| 0.2593        | 2.12  | 650  | 0.2066          | 0.9377    | 0.9400 | 0.9384      | 0.8444   |
| 0.2372        | 2.21  | 675  | 0.2240          | 0.9378    | 0.9406 | 0.9362      | 0.8225   |
| 0.2253        | 2.29  | 700  | 0.2325          | 0.9404    | 0.9419 | 0.9370      | 0.8237   |
| 0.2539        | 2.37  | 725  | 0.2218          | 0.9381    | 0.9400 | 0.9369      | 0.8400   |
| 0.2434        | 2.45  | 750  | 0.2260          | 0.9377    | 0.9394 | 0.9384      | 0.8404   |
| 0.2047        | 2.53  | 775  | 0.2320          | 0.9381    | 0.9394 | 0.9328      | 0.8009   |
| 0.2331        | 2.61  | 800  | 0.2094          | 0.9367    | 0.9394 | 0.9374      | 0.8325   |
| 0.2152        | 2.7   | 825  | 0.2365          | 0.9366    | 0.9387 | 0.9337      | 0.8157   |
| 0.2306        | 2.78  | 850  | 0.2675          | 0.9298    | 0.9324 | 0.9288      | 0.8219   |
| 0.2537        | 2.86  | 875  | 0.2271          | 0.9369    | 0.9394 | 0.9350      | 0.8215   |


### 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-80-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-80-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: 0.2271
* Precision: 0.9369
* Recall: 0.9394
* F1 Weighted: 0.9350
* F1 Macro: 0.8215


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