Instructions to use Israhassan/CoLAEncoderonly with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Israhassan/CoLAEncoderonly with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Israhassan/CoLAEncoderonly")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Israhassan/CoLAEncoderonly") model = AutoModelForSequenceClassification.from_pretrained("Israhassan/CoLAEncoderonly", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e4b4866ffd8cd4ca65d85cec8f44a06d38e9c51593cf1582327d9a675a5bdb13
- Size of remote file:
- 438 MB
- SHA256:
- b5be63449e38ecf9af5b907669a701a775fdd9ac5e8c22e8043e864352a5dc36
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.