Instructions to use Dwaraka/Sentence_Classification_CoLA_BERT_base_uncased_Encoder_Only_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dwaraka/Sentence_Classification_CoLA_BERT_base_uncased_Encoder_Only_Model with Transformers:
# 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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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datasets:
- Dwaraka/CoLA_raw_dataset
language:
- en
library_name: transformers
pipeline_tag: text-classification
---
# Sentence_Classification_CoLA_BERT_base_uncased_Encoder_Only_Model
This model is a fine tuned version of [bert_base_uncased](https://huggingface.co/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 |