Instructions to use Tordjx/FOMC_SENTIMENTS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tordjx/FOMC_SENTIMENTS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tordjx/FOMC_SENTIMENTS")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Tordjx/FOMC_SENTIMENTS") model = AutoModelForSequenceClassification.from_pretrained("Tordjx/FOMC_SENTIMENTS") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ba47e207ba0b1b6cc7883c37f454f33a3bd72daa66b82d9a0740e22e6791416c
- Size of remote file:
- 4.6 kB
- SHA256:
- b626f98e8e81d005dbb08394566fa10d70d3565f7c2491f138e57492255d4527
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.