Instructions to use Etelis/rtm_BERT_5E with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Etelis/rtm_BERT_5E with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Etelis/rtm_BERT_5E")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Etelis/rtm_BERT_5E") model = AutoModelForSequenceClassification.from_pretrained("Etelis/rtm_BERT_5E") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f1675d1d011ec43917940a3620cd58a2376b04edbfed34223013451f0f42c351
- Size of remote file:
- 3.38 kB
- SHA256:
- 72b6dfa5c89c469c2a3a568b7dd7732328f9a4f185b7751def040c09369ac14c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.