
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# distilbert-fa-zwnj-base-MLM-pquad

This model is pretained only on the [PQuAD](https://huggingface.co/datasets/Gholamreza/pquad) dataset. for educational purposes only.

Tokenizer and base model configs are from [HooshvareLab/distilbert-fa-zwnj-base](https://huggingface.co/HooshvareLab/distilbert-fa-zwnj-base) on the generator dataset.

## 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: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results
TrainOutput(global_step=31, training_loss=10.31849128969254, metrics={'train_runtime': 42.7618, 'train_samples_per_second': 188.369, 'train_steps_per_second': 0.725, 'total_flos': 263071290359808.0, 'train_loss': 10.31849128969254, 'epoch': 0.98})



### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["generator"], "model-index": [{"name": "distilbert-fa-zwnj-base-MLM-pquad", "results": []}]}
transformers pytorch tensorboard distilbert fill-mask generated_from_trainer dataset:generator license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-generator #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 




# distilbert-fa-zwnj-base-MLM-pquad

This model is pretained only on the PQuAD dataset. for educational purposes only.

Tokenizer and base model configs are from HooshvareLab/distilbert-fa-zwnj-base on the generator dataset.

## 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: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results
TrainOutput(global_step=31, training_loss=10.31849128969254, metrics={'train_runtime': 42.7618, 'train_samples_per_second': 188.369, 'train_steps_per_second': 0.725, 'total_flos': 263071290359808.0, 'train_loss': 10.31849128969254, 'epoch': 0.98})



### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

# distilbert-fa-zwnj-base-MLM-pquad

This model is pretained only on the PQuAD dataset. for educational purposes only.

Tokenizer and base model configs are from HooshvareLab/distilbert-fa-zwnj-base on the generator dataset. ## 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: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
- mixed_precision_training: Native AMP ### Training results
TrainOutput(global_step=31, training_loss=10.31849128969254, metrics={'train_runtime': 42.7618, 'train_samples_per_second': 188.369, 'train_steps_per_second': 0.725, 'total_flos': 263071290359808.0, 'train_loss': 10.31849128969254, 'epoch': 0.98}) ### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-generator #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # distilbert-fa-zwnj-base-MLM-pquad

This model is pretained only on the PQuAD dataset. for educational purposes only.

Tokenizer and base model configs are from HooshvareLab/distilbert-fa-zwnj-base on the generator dataset. ## 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: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
- mixed_precision_training: Native AMP ### Training results
TrainOutput(global_step=31, training_loss=10.31849128969254, metrics={'train_runtime': 42.7618, 'train_samples_per_second': 188.369, 'train_steps_per_second': 0.725, 'total_flos': 263071290359808.0, 'train_loss': 10.31849128969254, 'epoch': 0.98}) ### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-generator #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# distilbert-fa-zwnj-base-MLM-pquad

This model is pretained only on the PQuAD dataset. for educational purposes only.

Tokenizer and base model configs are from HooshvareLab/distilbert-fa-zwnj-base on the generator dataset.## 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: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
- mixed_precision_training: Native AMP### Training results
TrainOutput(global_step=31, training_loss=10.31849128969254, metrics={'train_runtime': 42.7618, 'train_samples_per_second': 188.369, 'train_steps_per_second': 0.725, 'total_flos': 263071290359808.0, 'train_loss': 10.31849128969254, 'epoch': 0.98})### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2