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
Download model.safetensors from Dwaraka/Sentence_Classification_CoLA_BERT_base_uncased_Encoder_Only_Model: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/Dwaraka/Sentence_Classification_CoLA_BERT_base_uncased_Encoder_Only_Model/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://Dwaraka/Sentence_Classification_CoLA_BERT_base_uncased_Encoder_Only_Model@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Dwaraka/Sentence_Classification_CoLA_BERT_base_uncased_Encoder_Only_Model/resolve/refs%2Fpr%2F1/model.safetensors
438 MB
- Xet hash:
- 1e435809616668e5ac9955b53b7164415c7e66ef410912927be6610c9b3e8f3b
- Size of remote file:
- 438 MB
- SHA256:
- aaae917ff2b7e52e154c842632eff37c95a4dd2cd727b3bdc79e783ad225ba33
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.