# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Dwaraka/Sentence_Classification_CoLA_BERT_base_uncased_Encoder_Only_Model")
model = AutoModelForSequenceClassification.from_pretrained("Dwaraka/Sentence_Classification_CoLA_BERT_base_uncased_Encoder_Only_Model")Quick Links
Sentence_Classification_CoLA_BERT_base_uncased_Encoder_Only_Model
This model is a fine tuned version of bert_base_uncased trained on GLUE Benchmarks' The Corpus of Linguistic Acceptability [CoLA] Dataset(https://nyu-mll.github.io/CoLA/).
This model is used to check if a sentence is grammatically correct or not.
Model Description:
This model
- Downloads last month
- 4
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Dwaraka/Sentence_Classification_CoLA_BERT_base_uncased_Encoder_Only_Model")