Instructions to use omarelsayeed/QA_CrossEncoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use omarelsayeed/QA_CrossEncoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="omarelsayeed/QA_CrossEncoder")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("omarelsayeed/QA_CrossEncoder") model = AutoModelForSequenceClassification.from_pretrained("omarelsayeed/QA_CrossEncoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d786d4e2b40a6f28a7fb77c2f78dbc04636cef0dad4b58600003af65e57618a0
- Size of remote file:
- 46.2 MB
- SHA256:
- 020f8409acbcf60bf206e022aa3c5085221d151447d4d8700a140ae89c1e748c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.