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# roberta

This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5264
- Ndcg: 0.4471
- Accuracy: 0.2959

## 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
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Ndcg   | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:--------:|
| 1.5844        | 0.07  | 413   | 1.5508          | 0.4255 | 0.2786   |
| 1.5709        | 0.13  | 826   | 1.5537          | 0.4204 | 0.2854   |
| 1.5581        | 0.2   | 1239  | 1.5557          | 0.4252 | 0.2777   |
| 1.5723        | 0.27  | 1652  | 1.5498          | 0.4329 | 0.2833   |
| 1.5543        | 0.33  | 2065  | 1.5430          | 0.4272 | 0.2872   |
| 1.549         | 0.4   | 2478  | 1.5391          | 0.4371 | 0.2899   |
| 1.5527        | 0.47  | 2891  | 1.5420          | 0.4328 | 0.2838   |
| 1.5583        | 0.53  | 3304  | 1.5430          | 0.4371 | 0.2882   |
| 1.537         | 0.6   | 3717  | 1.5371          | 0.4395 | 0.2904   |
| 1.5381        | 0.67  | 4130  | 1.5410          | 0.4391 | 0.2897   |
| 1.5876        | 0.73  | 4543  | 1.7871          | 0.3264 | 0.2005   |
| 1.5573        | 0.8   | 4956  | 1.5430          | 0.4400 | 0.2917   |
| 1.5577        | 0.87  | 5369  | 1.5576          | 0.4319 | 0.2830   |
| 1.5398        | 0.93  | 5782  | 1.5557          | 0.4140 | 0.2657   |
| 1.5414        | 1.0   | 6195  | 1.5986          | 0.4037 | 0.2617   |
| 1.5439        | 1.07  | 6608  | 1.5459          | 0.4303 | 0.2834   |
| 1.5344        | 1.13  | 7021  | 1.5439          | 0.4325 | 0.2838   |
| 1.544         | 1.2   | 7434  | 1.5498          | 0.4335 | 0.2861   |
| 1.5414        | 1.27  | 7847  | 1.5342          | 0.4422 | 0.2910   |
| 1.5358        | 1.33  | 8260  | 1.5312          | 0.4428 | 0.2940   |
| 1.5347        | 1.4   | 8673  | 1.5303          | 0.4457 | 0.2955   |
| 1.5305        | 1.47  | 9086  | 1.5283          | 0.4454 | 0.2944   |
| 1.5406        | 1.53  | 9499  | 1.5283          | 0.4462 | 0.2952   |
| 1.5316        | 1.6   | 9912  | 1.5283          | 0.4445 | 0.2949   |
| 1.5252        | 1.67  | 10325 | 1.5283          | 0.4465 | 0.2958   |
| 1.5202        | 1.73  | 10738 | 1.5264          | 0.4469 | 0.2949   |
| 1.5313        | 1.8   | 11151 | 1.5273          | 0.4469 | 0.2959   |
| 1.5279        | 1.87  | 11564 | 1.5264          | 0.4470 | 0.2963   |
| 1.5317        | 1.93  | 11977 | 1.5264          | 0.4474 | 0.2964   |
| 1.5208        | 2.0   | 12390 | 1.5264          | 0.4471 | 0.2959   |


### Framework versions

- Transformers 4.29.1
- Pytorch 2.0.0-rc1
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "roberta", "results": []}]}
transformers pytorch roberta text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

roberta
=======


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


* Loss: 1.5264
* Ndcg: 0.4471
* Accuracy: 0.2959


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
* distributed\_type: multi-GPU
* num\_devices: 4
* total\_train\_batch\_size: 64
* total\_eval\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* num\_epochs: 2


### Training results



### Framework versions


* Transformers 4.29.1
* Pytorch 2.0.0-rc1
* 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
* distributed\_type: multi-GPU
* num\_devices: 4
* total\_train\_batch\_size: 64
* total\_eval\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* num\_epochs: 2 ### Training results ### Framework versions


* Transformers 4.29.1
* Pytorch 2.0.0-rc1
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #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
* distributed\_type: multi-GPU
* num\_devices: 4
* total\_train\_batch\_size: 64
* total\_eval\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* num\_epochs: 2 ### Training results ### Framework versions


* Transformers 4.29.1
* Pytorch 2.0.0-rc1
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #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
* distributed\_type: multi-GPU
* num\_devices: 4
* total\_train\_batch\_size: 64
* total\_eval\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* num\_epochs: 2### Training results### Framework versions


* Transformers 4.29.1
* Pytorch 2.0.0-rc1
* Datasets 2.12.0
* Tokenizers 0.13.3