Instructions to use MidhunKanadan/SentimentBERT-AIWriting with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MidhunKanadan/SentimentBERT-AIWriting with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MidhunKanadan/SentimentBERT-AIWriting")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MidhunKanadan/SentimentBERT-AIWriting") model = AutoModelForSequenceClassification.from_pretrained("MidhunKanadan/SentimentBERT-AIWriting") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6281ec819edc16d42ca8c6564be5cf0cb24a31a9fe8e7642d667a8abb99bddeb
- Size of remote file:
- 438 MB
- SHA256:
- 4c63b7c7b158413c1ae24ac01ad1191ee3d0004835f04f3f2a4ac690df5f7942
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.