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# roberta-with-topic

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: 1.5283
- Ndcg: 0.4453
- Accuracy: 0.2941

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:--------:|
| 1.5951        | 0.07  | 413   | 1.5693          | 0.4220 | 0.2766   |
| 1.5721        | 0.13  | 826   | 1.5537          | 0.4308 | 0.2828   |
| 1.5594        | 0.2   | 1239  | 1.5615          | 0.4236 | 0.2757   |
| 1.5753        | 0.27  | 1652  | 1.5645          | 0.4272 | 0.2778   |
| 1.5778        | 0.33  | 2065  | 1.5859          | 0.3736 | 0.2430   |
| 1.5673        | 0.4   | 2478  | 1.5576          | 0.4262 | 0.2812   |
| 1.5633        | 0.47  | 2891  | 1.5557          | 0.4294 | 0.2815   |
| 1.5606        | 0.53  | 3304  | 1.5459          | 0.4321 | 0.2836   |
| 1.5476        | 0.6   | 3717  | 1.5508          | 0.4269 | 0.2810   |
| 1.552         | 0.67  | 4130  | 1.5479          | 0.4302 | 0.2831   |
| 1.5469        | 0.73  | 4543  | 1.5430          | 0.4345 | 0.2882   |
| 1.5538        | 0.8   | 4956  | 1.5410          | 0.4371 | 0.2877   |
| 1.557         | 0.87  | 5369  | 1.5420          | 0.4368 | 0.2896   |
| 1.5427        | 0.93  | 5782  | 1.5449          | 0.4269 | 0.2814   |
| 1.5427        | 1.0   | 6195  | 1.5381          | 0.4380 | 0.2896   |
| 1.5469        | 1.07  | 6608  | 1.5381          | 0.4362 | 0.2849   |
| 1.5369        | 1.13  | 7021  | 1.5361          | 0.4383 | 0.2895   |
| 1.5465        | 1.2   | 7434  | 1.5361          | 0.4415 | 0.2940   |
| 1.5433        | 1.27  | 7847  | 1.5342          | 0.4399 | 0.2914   |
| 1.5355        | 1.33  | 8260  | 1.5342          | 0.4409 | 0.2937   |
| 1.5363        | 1.4   | 8673  | 1.5342          | 0.4414 | 0.2923   |
| 1.5372        | 1.47  | 9086  | 1.5312          | 0.4440 | 0.2949   |
| 1.5452        | 1.53  | 9499  | 1.5303          | 0.4439 | 0.2937   |
| 1.5386        | 1.6   | 9912  | 1.5293          | 0.4434 | 0.2915   |
| 1.5314        | 1.67  | 10325 | 1.5303          | 0.4443 | 0.2925   |
| 1.5216        | 1.73  | 10738 | 1.5293          | 0.4447 | 0.2930   |
| 1.5341        | 1.8   | 11151 | 1.5293          | 0.4450 | 0.2929   |
| 1.5315        | 1.87  | 11564 | 1.5283          | 0.4456 | 0.2947   |
| 1.5345        | 1.93  | 11977 | 1.5283          | 0.4455 | 0.2950   |
| 1.5238        | 2.0   | 12390 | 1.5283          | 0.4453 | 0.2941   |


### 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-with-topic", "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-with-topic
==================


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


* Loss: 1.5283
* Ndcg: 0.4453
* Accuracy: 0.2941


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