Instructions to use sms1097/retrieval_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sms1097/retrieval_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sms1097/retrieval_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sms1097/retrieval_model") model = AutoModelForSequenceClassification.from_pretrained("sms1097/retrieval_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8d00a364b545d4a6f446c18bbf43cc2c6cdb687ccb93f42ef5808f1a2a20c91f
- Size of remote file:
- 4.66 kB
- SHA256:
- 38803918182cee9f58463331c538b1704c52fb48b49366dff7f35059b08fbf3e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.