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# bert_large_yc_recipe_30

This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000

## 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
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 1.0   | 121  | 0.0026          |
| No log        | 2.0   | 242  | 0.0006          |
| No log        | 3.0   | 363  | 0.0001          |
| No log        | 4.0   | 484  | 0.0000          |
| 0.057         | 5.0   | 605  | 0.0000          |
| 0.057         | 6.0   | 726  | 0.0000          |
| 0.057         | 7.0   | 847  | 0.0000          |
| 0.057         | 8.0   | 968  | 0.0000          |
| 0.0002        | 9.0   | 1089 | 0.0001          |
| 0.0002        | 10.0  | 1210 | 0.0001          |
| 0.0002        | 11.0  | 1331 | 0.0002          |
| 0.0002        | 12.0  | 1452 | 0.0001          |
| 0.0002        | 13.0  | 1573 | 0.0001          |
| 0.0002        | 14.0  | 1694 | 0.0001          |
| 0.0002        | 15.0  | 1815 | 0.0001          |
| 0.0002        | 16.0  | 1936 | 0.0001          |
| 0.0           | 17.0  | 2057 | 0.0001          |
| 0.0           | 18.0  | 2178 | 0.0001          |
| 0.0           | 19.0  | 2299 | 0.0001          |
| 0.0           | 20.0  | 2420 | 0.0001          |
| 0.0           | 21.0  | 2541 | 0.0001          |
| 0.0           | 22.0  | 2662 | 0.0001          |
| 0.0           | 23.0  | 2783 | 0.0001          |
| 0.0           | 24.0  | 2904 | 0.0001          |
| 0.0           | 25.0  | 3025 | 0.0000          |
| 0.0           | 26.0  | 3146 | 0.0000          |
| 0.0           | 27.0  | 3267 | 0.0000          |
| 0.0           | 28.0  | 3388 | 0.0000          |
| 0.0           | 29.0  | 3509 | 0.0000          |
| 0.0           | 30.0  | 3630 | 0.0000          |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.11.0a0+17540c5
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert_large_yc_recipe_30", "results": []}]}
transformers pytorch tensorboard bert fill-mask generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

bert\_large\_yc\_recipe\_30
===========================


This model is a fine-tuned version of bert-large-uncased on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0000


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
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.21.1
* Pytorch 1.11.0a0+17540c5
* Datasets 2.4.0
* 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
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.11.0a0+17540c5
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #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
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.11.0a0+17540c5
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #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
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.21.1
* Pytorch 1.11.0a0+17540c5
* Datasets 2.4.0
* Tokenizers 0.12.1