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# roberta-base-culinary-finetuned

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0657
- F1: 0.9929

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step  | Validation Loss | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.1803        | 0.11  | 500   | 0.1939          | 0.9611 |
| 0.1543        | 0.22  | 1000  | 0.1364          | 0.9669 |
| 0.1213        | 0.32  | 1500  | 0.1487          | 0.9728 |
| 0.1079        | 0.43  | 2000  | 0.0855          | 0.9773 |
| 0.0975        | 0.54  | 2500  | 0.0844          | 0.9831 |
| 0.0855        | 0.65  | 3000  | 0.0785          | 0.9831 |
| 0.0844        | 0.76  | 3500  | 0.0679          | 0.9857 |
| 0.0793        | 0.86  | 4000  | 0.0489          | 0.9890 |
| 0.0864        | 0.97  | 4500  | 0.0399          | 0.9903 |
| 0.049         | 1.08  | 5000  | 0.0528          | 0.9890 |
| 0.0353        | 1.19  | 5500  | 0.0635          | 0.9877 |
| 0.0321        | 1.3   | 6000  | 0.0542          | 0.9903 |
| 0.0311        | 1.41  | 6500  | 0.0559          | 0.9896 |
| 0.0315        | 1.51  | 7000  | 0.0736          | 0.9857 |
| 0.04          | 1.62  | 7500  | 0.0648          | 0.9909 |
| 0.0265        | 1.73  | 8000  | 0.0608          | 0.9909 |
| 0.0443        | 1.84  | 8500  | 0.0617          | 0.9883 |
| 0.0443        | 1.95  | 9000  | 0.0555          | 0.9896 |
| 0.0235        | 2.05  | 9500  | 0.0608          | 0.9903 |
| 0.0139        | 2.16  | 10000 | 0.0613          | 0.9922 |
| 0.0126        | 2.27  | 10500 | 0.0739          | 0.9903 |
| 0.0164        | 2.38  | 11000 | 0.0679          | 0.9903 |
| 0.0172        | 2.49  | 11500 | 0.0606          | 0.9922 |
| 0.0175        | 2.59  | 12000 | 0.0442          | 0.9942 |
| 0.01          | 2.7   | 12500 | 0.0661          | 0.9916 |
| 0.0059        | 2.81  | 13000 | 0.0659          | 0.9929 |
| 0.0216        | 2.92  | 13500 | 0.0504          | 0.9929 |
| 0.0123        | 3.03  | 14000 | 0.0584          | 0.9929 |
| 0.0047        | 3.14  | 14500 | 0.0573          | 0.9929 |
| 0.0123        | 3.24  | 15000 | 0.0511          | 0.9935 |
| 0.0027        | 3.35  | 15500 | 0.0579          | 0.9942 |
| 0.0025        | 3.46  | 16000 | 0.0602          | 0.9935 |
| 0.0051        | 3.57  | 16500 | 0.0598          | 0.9935 |
| 0.0044        | 3.68  | 17000 | 0.0617          | 0.9929 |
| 0.0061        | 3.78  | 17500 | 0.0634          | 0.9935 |
| 0.0048        | 3.89  | 18000 | 0.0672          | 0.9929 |
| 0.0078        | 4.0   | 18500 | 0.0657          | 0.9929 |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1

{"tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "roberta-base-culinary-finetuned", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

roberta-base-culinary-finetuned
===============================


This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0657
* F1: 0.9929


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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4


### Training results



### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4### Training results### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.12.1