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# he-ru-lid

This model is a fine-tuned version of [papluca/xlm-roberta-base-language-detection](https://huggingface.co/papluca/xlm-roberta-base-language-detection) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3460
- Precision: 0.9258
- Recall: 0.8674
- F1: 0.8956
- Accuracy: 0.9705

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0904        | 1.0   | 563  | 0.0069          | 0.9902    | 0.9959 | 0.9931 | 0.9984   |
| 0.0101        | 2.0   | 1126 | 0.0030          | 0.9959    | 0.9973 | 0.9966 | 0.9994   |
| 0.0047        | 3.0   | 1689 | 0.0019          | 0.9981    | 0.9983 | 0.9982 | 0.9996   |
| 0.0025        | 4.0   | 2252 | 0.0017          | 0.9983    | 0.9981 | 0.9982 | 0.9997   |
| 0.002         | 5.0   | 2815 | 0.0016          | 0.9979    | 0.9983 | 0.9981 | 0.9997   |
| 0.0012        | 6.0   | 3378 | 0.0020          | 0.9981    | 0.9990 | 0.9986 | 0.9997   |
| 0.001         | 7.0   | 3941 | 0.0023          | 0.9986    | 0.9992 | 0.9989 | 0.9998   |
| 0.0007        | 8.0   | 4504 | 0.0013          | 0.9990    | 0.9994 | 0.9992 | 0.9998   |
| 0.0004        | 9.0   | 5067 | 0.0012          | 0.9994    | 0.9994 | 0.9994 | 0.9999   |
| 0.0005        | 10.0  | 5630 | 0.0012          | 0.9994    | 0.9994 | 0.9994 | 0.9999   |
| 0.0003        | 11.0  | 6193 | 0.0012          | 0.9994    | 0.9994 | 0.9994 | 0.9999   |
| 0.0003        | 12.0  | 6756 | 0.0012          | 0.9994    | 0.9994 | 0.9994 | 0.9999   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "he-ru-lid", "results": []}]}
transformers pytorch tensorboard xlm-roberta token-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

he-ru-lid
=========


This model is a fine-tuned version of papluca/xlm-roberta-base-language-detection on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3460
* Precision: 0.9258
* Recall: 0.8674
* F1: 0.8956
* Accuracy: 0.9705


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: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 12


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 12 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 12 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 12### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3