Instructions to use ganjubas2008/absa-rubert-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ganjubas2008/absa-rubert-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ganjubas2008/absa-rubert-tiny")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ganjubas2008/absa-rubert-tiny") model = AutoModelForSequenceClassification.from_pretrained("ganjubas2008/absa-rubert-tiny") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cc247154105556415f170be9c9807961e55e5977dcbaec4bda3dc9fe9c9fcd70
- Size of remote file:
- 117 MB
- SHA256:
- 2ee1a572002077518aa9354b2a3ce7799fe86dc29db6ac7dc83b1a7372d6e633
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.