license: apache-2.0
inference: false
datasets: google_wellformed_query
@misc {ashish_kumar_2024,
author = { {Ashish Kumar} },
title = { query_wellformedness_score (Revision 55a424c) },
year = 2024,
url = { https://huggingface.co/Ashishkr/query_wellformedness_score },
doi = { 10.57967/hf/1980 },
publisher = { Hugging Face }
}
Intended Use Cases
Content Creation: Validate the well-formedness of written content.
Educational Platforms: Helps students check the grammaticality of their sentences.
Chatbots & Virtual Assistants: To validate user queries or generate well-formed responses.
contact: kua613@g.harvard.edu
Model name: Query Wellformedness Scoring
Description : Evaluate the well-formedness of sentences by checking grammatical correctness and completeness. Sensitive to case and penalizes sentences for incorrect grammar and case.
Features:
- Wellformedness Score: Provides a score indicating grammatical correctness and completeness.
- Case Sensitivity: Recognizes and penalizes incorrect casing in sentences.
- Broad Applicability: Can be used on a wide range of sentences.
Example:
- Dogs are mammals.
- she loves to read books on history.
- When the rain in Spain.
- Eating apples are healthy for you.
- The Eiffel Tower is in Paris.
Among these sentences: Sentences 1 and 5 are well-formed and have correct grammar and case. Sentence 2 starts with a lowercase letter. Sentence 3 is a fragment and is not well-formed. Sentence 4 has a subject-verb agreement error.
example_usage: library: HuggingFace transformers
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Ashishkr/query_wellformedness_score")
model = AutoModelForSequenceClassification.from_pretrained("Ashishkr/query_wellformedness_score")
sentences = [
"The quarterly financial report are showing an increase.", # Incorrect
"Him has completed the audit for last fiscal year.", # Incorrect
"Please to inform the board about the recent developments.", # Incorrect
"The team successfully achieved all its targets for the last quarter.", # Correct
"Our company is exploring new ventures in the European market." # Correct
]
features = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = model(**features).logits
print(scores)
Cite Ashishkr/query_wellformedness_score