# VizWiz-Bert model (uncased)
Fine-tuning the BERT-base model for the Fill Mask task in [VizWiz-Vision Skills for VQA](https://vizwiz.org/tasks-and-datasets/vision-skills/)

  ## Fine-tuining information
  * model: **bert-base-uncased**
  * downstream_tasks: **fill-mask**
  
  ## Dataset information
  * [annotations](https://github.com/chiutaiyin/Vision-Skills/tree/master/csv) (CSV files)
  * size: **~22K**
  * max_token_len: **78**
  ![token_dist](tks_dist.png)
  
  ## Training information
  * random_seed: **16**
  * max_token_len: **78**
  * train_batch_size: **32** 
  * val_batch_size: **16**
  * num_epochs: **5**, 
  * learning_rate: **5e-06**, 
  * split_train: **0.8**
  * optimizer: **adamw**
  
  ## Learning curves
  ![learning_curves](lc.png)

{"language": ["en"], "license": "mit"}
transformers pytorch bert fill-mask en license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #fill-mask #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 

# VizWiz-Bert model (uncased)
Fine-tuning the BERT-base model for the Fill Mask task in VizWiz-Vision Skills for VQA

  ## Fine-tuining information
  * model: bert-base-uncased
  * downstream_tasks: fill-mask
  
  ## Dataset information
  * annotations (CSV files)
  * size: ~22K
  * max_token_len: 78
  !token_dist
  
  ## Training information
  * random_seed: 16
  * max_token_len: 78
  * train_batch_size: 32 
  * val_batch_size: 16
  * num_epochs: 5, 
  * learning_rate: 5e-06, 
  * split_train: 0.8
  * optimizer: adamw
  
  ## Learning curves
  !learning_curves

# VizWiz-Bert model (uncased)
Fine-tuning the BERT-base model for the Fill Mask task in VizWiz-Vision Skills for VQA

  ## Fine-tuining information
  * model: bert-base-uncased
  * downstream_tasks: fill-mask
  
  ## Dataset information
  * annotations (CSV files)
  * size: ~22K
  * max_token_len: 78
  !token_dist
  
  ## Training information
  * random_seed: 16
  * max_token_len: 78
  * train_batch_size: 32 
  * val_batch_size: 16
  * num_epochs: 5, 
  * learning_rate: 5e-06, 
  * split_train: 0.8
  * optimizer: adamw
  
  ## Learning curves
  !learning_curves
TAGS
#transformers #pytorch #bert #fill-mask #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # VizWiz-Bert model (uncased)
Fine-tuning the BERT-base model for the Fill Mask task in VizWiz-Vision Skills for VQA

  ## Fine-tuining information
  * model: bert-base-uncased
  * downstream_tasks: fill-mask
  
  ## Dataset information
  * annotations (CSV files)
  * size: ~22K
  * max_token_len: 78
  !token_dist
  
  ## Training information
  * random_seed: 16
  * max_token_len: 78
  * train_batch_size: 32 
  * val_batch_size: 16
  * num_epochs: 5, 
  * learning_rate: 5e-06, 
  * split_train: 0.8
  * optimizer: adamw
  
  ## Learning curves
  !learning_curves