## How to use:

```
from transformers import AutoModelForMaskedLM, AutoTokenizer, pipeline

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("yashpatil/biobert-tiny-tokenizer")
model = AutoModelForMaskedLM.from_pretrained("yashpatil/biobert-tiny-model")

unmasker = pipeline('fill-mask', model=model, tokenizer=tokenizer)
output = unmasker("[MASK] are tiny organelles inside cells that are involved in releasing energy from food. This process is known as cellular respiration.")

# Print output
for word in output:
    print(word['token_str'], word['score'])
```
{}
transformers pytorch bert fill-mask autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us 

## How to use:


## How to use:
TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us 
 ## How to use:
TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us 
## How to use: