Instructions to use stanford-nlpxed/solution with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stanford-nlpxed/solution with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="stanford-nlpxed/solution")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("stanford-nlpxed/solution") model = AutoModelForSequenceClassification.from_pretrained("stanford-nlpxed/solution") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 43fa0218fe49a4f95ce4009c6ad68b95ecbe13fec5f47232422e149aa655fb65
- Size of remote file:
- 4.09 kB
- SHA256:
- d0033249f8d794bd43f9f3bca42a79e92350b7b0bcba11a9c4dd64a8046a1c9f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.