Instructions to use MENG21/studfacultyeval-BERT-LARGE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MENG21/studfacultyeval-BERT-LARGE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MENG21/studfacultyeval-BERT-LARGE")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MENG21/studfacultyeval-BERT-LARGE") model = AutoModelForSequenceClassification.from_pretrained("MENG21/studfacultyeval-BERT-LARGE") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 668aa1eb00acc2e807058d97c70407fb8ac38393c9e48b867f345d2104c66f3f
- Size of remote file:
- 1.34 GB
- SHA256:
- 8e0ce5a4a0624919377ab0a33f2630bbaaf89b4f095f37c9f3cdccc20ab09c92
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.