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# distilbert_add_pre-training-dim-96

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wikitext wikitext-103-raw-v1 dataset.
It achieves the following results on the evaluation set:
- Loss: 6.6092
- Accuracy: 0.1494

## 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: 64
- eval_batch_size: 64
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 14.685        | 1.0   | 3573  | 9.3922          | 0.1240   |
| 8.0255        | 2.0   | 7146  | 7.1510          | 0.1315   |
| 7.0152        | 3.0   | 10719 | 6.7861          | 0.1482   |
| 6.8127        | 4.0   | 14292 | 6.7053          | 0.1493   |
| 6.74          | 5.0   | 17865 | 6.6695          | 0.1474   |
| 6.7067        | 6.0   | 21438 | 6.6431          | 0.1491   |
| 6.6871        | 7.0   | 25011 | 6.6204          | 0.1483   |
| 6.6748        | 8.0   | 28584 | 6.6250          | 0.1473   |
| 6.6649        | 9.0   | 32157 | 6.6108          | 0.1486   |
| 6.6596        | 10.0  | 35730 | 6.6140          | 0.1497   |
| 6.6536        | 11.0  | 39303 | 6.6067          | 0.1493   |
| 6.6483        | 12.0  | 42876 | 6.6140          | 0.1489   |
| 6.6463        | 13.0  | 46449 | 6.6096          | 0.1484   |
| 6.6434        | 14.0  | 50022 | 6.5570          | 0.1526   |
| 6.6414        | 15.0  | 53595 | 6.5836          | 0.1526   |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wikitext"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert_add_pre-training-dim-96", "results": [{"task": {"type": "fill-mask", "name": "Masked Language Modeling"}, "dataset": {"name": "wikitext wikitext-103-raw-v1", "type": "wikitext", "config": "wikitext-103-raw-v1", "split": "validation", "args": "wikitext-103-raw-v1"}, "metrics": [{"type": "accuracy", "value": 0.14942141332434558, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard distilbert fill-mask generated_from_trainer dataset:wikitext license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-wikitext #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

distilbert\_add\_pre-training-dim-96
====================================


This model is a fine-tuned version of distilbert-base-uncased on the wikitext wikitext-103-raw-v1 dataset.
It achieves the following results on the evaluation set:


* Loss: 6.6092
* Accuracy: 0.1494


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: 64
* eval\_batch\_size: 64
* seed: 10
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 15
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.26.0
* Pytorch 1.14.0a0+410ce96
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 10
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 15
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.14.0a0+410ce96
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-wikitext #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 10
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 15
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.14.0a0+410ce96
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-wikitext #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 10
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 15
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.26.0
* Pytorch 1.14.0a0+410ce96
* Datasets 2.9.0
* Tokenizers 0.13.2