|
--- |
|
license: apache-2.0 |
|
inference: false |
|
datasets: google_wellformed_query |
|
--- |
|
|
|
|
|
```DOI |
|
|
|
|
|
@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**: |
|
1. Dogs are mammals. |
|
2. she loves to read books on history. |
|
3. When the rain in Spain. |
|
4. Eating apples are healthy for you. |
|
5. 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* |
|
```python |
|
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 |
|
|
|
|
|
|
|
|
|
|
|
|
