Instructions to use Bhuvana/t5-base-spellchecker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bhuvana/t5-base-spellchecker with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Bhuvana/t5-base-spellchecker") model = AutoModelForSeq2SeqLM.from_pretrained("Bhuvana/t5-base-spellchecker") - Notebooks
- Google Colab
- Kaggle
Quick Links
Spell checker using T5 base transformer
A simple spell checker built using T5-Base transformer. To use this model
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Bhuvana/t5-base-spellchecker")
model = AutoModelForSeq2SeqLM.from_pretrained("Bhuvana/t5-base-spellchecker")
def correct(inputs):
input_ids = tokenizer.encode(inputs,return_tensors='pt')
sample_output = model.generate(
input_ids,
do_sample=True,
max_length=50,
top_p=0.99,
num_return_sequences=1
)
res = tokenizer.decode(sample_output[0], skip_special_tokens=True)
return res
text = "christmas is celbrated on decembr 25 evry ear"
print(correct(text))
This should print the corrected statement
christmas is celebrated on december 25 every year
You can also type the text under the Hosted inference API and get predictions online.
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# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Bhuvana/t5-base-spellchecker") model = AutoModelForSeq2SeqLM.from_pretrained("Bhuvana/t5-base-spellchecker")