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# bert-small-finer-longer

This model is a fine-tuned version of [muhtasham/bert-small-finer](https://huggingface.co/muhtasham/bert-small-finer) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4264

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| No log        | 0.49  | 500   | 1.6683          |
| 1.5941        | 0.97  | 1000  | 1.6569          |
| 1.5941        | 1.46  | 1500  | 1.6436          |
| 1.5605        | 1.94  | 2000  | 1.6173          |
| 1.5605        | 2.43  | 2500  | 1.6073          |
| 1.5297        | 2.91  | 3000  | 1.6001          |
| 1.5297        | 3.4   | 3500  | 1.5815          |
| 1.5022        | 3.89  | 4000  | 1.5756          |
| 1.5022        | 4.37  | 4500  | 1.5568          |
| 1.4753        | 4.86  | 5000  | 1.5458          |
| 1.4753        | 5.34  | 5500  | 1.5399          |
| 1.4537        | 5.83  | 6000  | 1.5273          |
| 1.4537        | 6.32  | 6500  | 1.5192          |
| 1.433         | 6.8   | 7000  | 1.5099          |
| 1.433         | 7.29  | 7500  | 1.5083          |
| 1.4169        | 7.77  | 8000  | 1.4957          |
| 1.4169        | 8.26  | 8500  | 1.4914          |
| 1.3982        | 8.75  | 9000  | 1.4859          |
| 1.3982        | 9.23  | 9500  | 1.4697          |
| 1.3877        | 9.72  | 10000 | 1.4711          |
| 1.3877        | 10.2  | 10500 | 1.4608          |
| 1.3729        | 10.69 | 11000 | 1.4583          |
| 1.3729        | 11.18 | 11500 | 1.4513          |
| 1.3627        | 11.66 | 12000 | 1.4498          |
| 1.3627        | 12.15 | 12500 | 1.4396          |
| 1.357         | 12.63 | 13000 | 1.4415          |
| 1.357         | 13.12 | 13500 | 1.4347          |
| 1.3484        | 13.61 | 14000 | 1.4316          |
| 1.3484        | 14.09 | 14500 | 1.4319          |
| 1.3442        | 14.58 | 15000 | 1.4268          |
| 1.3442        | 15.06 | 15500 | 1.4293          |
| 1.3387        | 15.55 | 16000 | 1.4217          |
| 1.3387        | 16.03 | 16500 | 1.4241          |
| 1.3358        | 16.52 | 17000 | 1.4250          |
| 1.3358        | 17.01 | 17500 | 1.4196          |
| 1.3344        | 17.49 | 18000 | 1.4193          |
| 1.3344        | 17.98 | 18500 | 1.4200          |
| 1.3274        | 18.46 | 19000 | 1.4250          |
| 1.3274        | 18.95 | 19500 | 1.4168          |
| 1.3348        | 19.44 | 20000 | 1.4164          |
| 1.3348        | 19.92 | 20500 | 1.4264          |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-small-finer-longer", "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-small-finer-longer
=======================


This model is a fine-tuned version of muhtasham/bert-small-finer on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.4264


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: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* num\_epochs: 20


### Training results



### Framework versions


* Transformers 4.18.0
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.12.1+cu113
* 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: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.12.1+cu113
* 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: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* num\_epochs: 20### Training results### Framework versions


* Transformers 4.18.0
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1