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")# 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") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -5,4 +5,15 @@ language:
|
|
| 5 |
- en
|
| 6 |
library_name: transformers
|
| 7 |
pipeline_tag: text-classification
|
| 8 |
-
---
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 5 |
- en
|
| 6 |
library_name: transformers
|
| 7 |
pipeline_tag: text-classification
|
| 8 |
+
---
|
| 9 |
+
|
| 10 |
+
# Sentence_Classification_CoLA_BERT_base_uncased_Encoder_Only_Model
|
| 11 |
+
|
| 12 |
+
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
|
| 13 |
+
[CoLA] Dataset(https://nyu-mll.github.io/CoLA/).
|
| 14 |
+
|
| 15 |
+
This model is used to check if a sentence is grammatically correct or not.
|
| 16 |
+
|
| 17 |
+
## Model Description:
|
| 18 |
+
|
| 19 |
+
This model
|