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# film95000distilbert-base-uncased

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8725

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.7724        | 0.34  | 500   | 2.5848          |
| 2.5813        | 0.67  | 1000  | 2.4469          |
| 2.479         | 1.01  | 1500  | 2.3841          |
| 2.3872        | 1.35  | 2000  | 2.3378          |
| 2.3504        | 1.68  | 2500  | 2.2838          |
| 2.3134        | 2.02  | 3000  | 2.2451          |
| 2.2483        | 2.36  | 3500  | 2.1953          |
| 2.2166        | 2.69  | 4000  | 2.1854          |
| 2.2023        | 3.03  | 4500  | 2.1559          |
| 2.1438        | 3.37  | 5000  | 2.1479          |
| 2.1271        | 3.7   | 5500  | 2.1155          |
| 2.1092        | 4.04  | 6000  | 2.0980          |
| 2.0656        | 4.38  | 6500  | 2.0736          |
| 2.0544        | 4.71  | 7000  | 2.0567          |
| 2.037         | 5.05  | 7500  | 2.0234          |
| 1.9902        | 5.39  | 8000  | 2.0079          |
| 1.9883        | 5.72  | 8500  | 1.9988          |
| 1.9624        | 6.06  | 9000  | 1.9832          |
| 1.9348        | 6.4   | 9500  | 1.9643          |
| 1.9215        | 6.73  | 10000 | 1.9471          |
| 1.9103        | 7.07  | 10500 | 1.9434          |
| 1.8794        | 7.41  | 11000 | 1.9282          |
| 1.8762        | 7.74  | 11500 | 1.9194          |
| 1.8597        | 8.08  | 12000 | 1.9260          |
| 1.8402        | 8.42  | 12500 | 1.8795          |
| 1.8326        | 8.75  | 13000 | 1.8948          |
| 1.8191        | 9.09  | 13500 | 1.9020          |
| 1.8058        | 9.43  | 14000 | 1.8806          |
| 1.804         | 9.76  | 14500 | 1.8680          |


### 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": "film95000distilbert-base-uncased", "results": []}]}
transformers pytorch tensorboard distilbert fill-mask generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

film95000distilbert-base-uncased
================================


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


* Loss: 1.8725


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 #distilbert #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 #distilbert #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