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# bookmebus-roberta-2

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1144
- Accuracy: 0.5

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 249  | 1.2029          | 0.4931   |
| No log        | 2.0   | 498  | 1.3241          | 0.5347   |
| 0.9919        | 3.0   | 747  | 1.3590          | 0.5      |
| 0.9919        | 4.0   | 996  | 1.4430          | 0.5139   |
| 0.7674        | 5.0   | 1245 | 1.5039          | 0.5486   |
| 0.7674        | 6.0   | 1494 | 1.6894          | 0.5208   |
| 0.5381        | 7.0   | 1743 | 1.9312          | 0.4861   |
| 0.5381        | 8.0   | 1992 | 1.9636          | 0.5208   |
| 0.3594        | 9.0   | 2241 | 2.0836          | 0.5      |
| 0.3594        | 10.0  | 2490 | 2.1144          | 0.5      |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "widget": [{"text": "\u17a2\u17d2\u1793\u1780\u1794\u17be\u1780\u17a1\u17b6\u1793\u1798\u17b7\u1793\u1782\u17c4\u179a\u1796\u1785\u17d2\u1794\u17b6\u1794\u17cb"}, {"text": "\u179a\u17a0\u17d0\u179f\u1791\u17b6\u1793\u17cb\u1785\u17b7\u178f\u17d2\u178f"}], "model-index": [{"name": "bookmebus-roberta-2", "results": []}]}
transformers pytorch tensorboard xlm-roberta text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

bookmebus-roberta-2
===================


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


* Loss: 2.1144
* Accuracy: 0.5


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: 10


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+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: 10 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #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: 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: 10 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3