Ashishkr/query_wellformedness_score

Author: Ashish Kumar, Head of Platforms, AI & Data at Tata Group · ashishkumar.ai · Wikidata



@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

加载中…

@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 } }

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)