Instructions to use jhu-clsp/mmBERT-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jhu-clsp/mmBERT-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jhu-clsp/mmBERT-small")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/mmBERT-small") model = AutoModelForMaskedLM.from_pretrained("jhu-clsp/mmBERT-small", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Update pipeline tag to feature-extraction
#8
by nielsr HF Staff - opened
This PR updates the pipeline_tag for the mmBERT-base model from fill-mask to feature-extraction.
As an encoder-only model primarily used for tasks like classification, embedding, and retrieval, feature-extraction more accurately reflects its core functionality and common usage on the Hugging Face Hub. This change will help users discover the model more effectively for embedding and general feature extraction tasks.
The existing content, including the detailed usage examples and links, remains unchanged.