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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1e435809616668e5ac9955b53b7164415c7e66ef410912927be6610c9b3e8f3b
- Size of remote file:
- 438 MB
- SHA256:
- aaae917ff2b7e52e154c842632eff37c95a4dd2cd727b3bdc79e783ad225ba33
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