metadata
tags: salesken
license: apache-2.0
inference: true
datasets: google_wellformed_query
widget:
- text: what was the reason for everyone for leave the company
This model evaluates the wellformedness (non-fragment, grammatically correct) score of a sentence. Model is case-sensitive and penalises for incorrect case and grammar as well.
['She is presenting a paper tomorrow','she is presenting a paper tomorrow','She present paper today']
[[0.8917],[0.4270],[0.0134]]
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("salesken/query_wellformedness_score")
model = AutoModelForSequenceClassification.from_pretrained("salesken/query_wellformedness_score")
sentences = [' what was the reason for everyone to leave the company ',
' What was the reason behind everyone leaving the company ',
' why was everybody leaving the company ',
' what was the reason to everyone leave the company ',
' what be the reason for everyone to leave the company ',
' what was the reasons for everyone to leave the company ',
' what were the reasons for everyone to leave the company ']
features = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = model(**features).logits
print(scores)