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# Mara_DistilBert_Pretrained

DistilBERT, a variant of BERT, was employed to pre-trained a Marathi language model from scratch using one million sentences. This compact yet powerful model utilizes a distilled version of BERT's transformer architecture
- Loss: 7.4249
- 
## Examples
माझं प्रिय मित्र [MASK] आहे
  

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Examples
माझं प्रिय मित्र [MASK] आहे
### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 7.9421        | 0.84  | 1000 | 7.4249          |


### Framework versions

- Transformers 4.18.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.12.1
{"language": ["mr"], "tags": ["generated_from_trainer"], "pipeline_tag": "fill-mask", "model-index": [{"name": "Mara_DistilBert_Pretrained", "results": []}]}
transformers pytorch tensorboard distilbert fill-mask generated_from_trainer mr autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #mr #autotrain_compatible #endpoints_compatible #region-us 

Mara\_DistilBert\_Pretrained
============================


DistilBERT, a variant of BERT, was employed to pre-trained a Marathi language model from scratch using one million sentences. This compact yet powerful model utilizes a distilled version of BERT's transformer architecture


* Loss: 7.4249
* 


Examples
--------


माझं प्रिय मित्र [MASK] आहे


Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


More information needed


Examples
--------


माझं प्रिय मित्र [MASK] आहे


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 512
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1


### Training results



### Framework versions


* Transformers 4.18.0
* Pytorch 2.0.0
* Datasets 2.1.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 512
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 2.0.0
* Datasets 2.1.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #mr #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: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 512
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 2.0.0
* Datasets 2.1.0
* Tokenizers 0.12.1