Instructions to use ExponentialScience/LedgerBERT-Market-Sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ExponentialScience/LedgerBERT-Market-Sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ExponentialScience/LedgerBERT-Market-Sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ExponentialScience/LedgerBERT-Market-Sentiment") model = AutoModelForSequenceClassification.from_pretrained("ExponentialScience/LedgerBERT-Market-Sentiment") - Notebooks
- Google Colab
- Kaggle
Add metadata for license, library, and pipeline tag and add paper/code links
#1
by nielsr HF Staff - opened
Hi! I'm Niels from the Hugging Face community science team. I've opened this PR to enhance the model card with standardized metadata and improve its documentation.
Specifically, I've:
- Added
library_name: transformersto enable the "Use in Transformers" button and automated code snippets. - Added
license: cc-by-nc-4.0to the metadata for proper indexing. - Added
pipeline_tag: text-classificationfor better discoverability in the Hub's model gallery. - Included links to the original paper and the official GitHub repository at the top of the card.
- Fixed the label mapping in the "How to Use" Python snippet to align with the model's actual configuration.
These updates help users find, understand, and use your model more effectively!
walterhernandez changed pull request status to merged