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# film95000bert-base-uncased

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7698

## 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: 0.0002
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 14840

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.5616        | 0.34  | 500   | 2.3892          |
| 2.4245        | 0.67  | 1000  | 2.2938          |
| 2.3509        | 1.01  | 1500  | 2.2346          |
| 2.2721        | 1.35  | 2000  | 2.1967          |
| 2.2412        | 1.68  | 2500  | 2.1570          |
| 2.2005        | 2.02  | 3000  | 2.1281          |
| 2.1417        | 2.36  | 3500  | 2.0986          |
| 2.1257        | 2.69  | 4000  | 2.0741          |
| 2.0993        | 3.03  | 4500  | 2.0584          |
| 2.053         | 3.37  | 5000  | 2.0294          |
| 2.0364        | 3.7   | 5500  | 2.0049          |
| 2.0128        | 4.04  | 6000  | 1.9917          |
| 1.9605        | 4.38  | 6500  | 1.9668          |
| 1.9539        | 4.71  | 7000  | 1.9509          |
| 1.9349        | 5.05  | 7500  | 1.9346          |
| 1.8981        | 5.39  | 8000  | 1.9124          |
| 1.8874        | 5.72  | 8500  | 1.8990          |
| 1.8661        | 6.06  | 9000  | 1.8831          |
| 1.8295        | 6.4   | 9500  | 1.8648          |
| 1.8163        | 6.73  | 10000 | 1.8464          |
| 1.8021        | 7.07  | 10500 | 1.8448          |
| 1.7733        | 7.41  | 11000 | 1.8305          |
| 1.778         | 7.74  | 11500 | 1.8108          |
| 1.7493        | 8.08  | 12000 | 1.8044          |
| 1.7235        | 8.42  | 12500 | 1.7883          |
| 1.719         | 8.75  | 13000 | 1.7876          |
| 1.7029        | 9.09  | 13500 | 1.7784          |
| 1.6923        | 9.43  | 14000 | 1.7654          |
| 1.6913        | 9.76  | 14500 | 1.7684          |


### Framework versions

- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "film95000bert-base-uncased", "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 

film95000bert-base-uncased
==========================


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


* Loss: 1.7698


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: 0.0002
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 14840


### Training results



### Framework versions


* Transformers 4.27.3
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 14840 ### Training results ### Framework versions


* Transformers 4.27.3
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
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: 0.0002
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 14840 ### Training results ### Framework versions


* Transformers 4.27.3
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
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: 0.0002
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 14840### Training results### Framework versions


* Transformers 4.27.3
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2