Feature Extraction
Transformers
PyTorch
ONNX
Safetensors
Turkish
English
modernbert
fill-mask
turkish
legal
turkish-legal
mecellem
TRUBA
MN5
text-embeddings-inference
Instructions to use newmindai/Mursit-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use newmindai/Mursit-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="newmindai/Mursit-Base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("newmindai/Mursit-Base") model = AutoModelForMaskedLM.from_pretrained("newmindai/Mursit-Base") - Notebooks
- Google Colab
- Kaggle
Add library_name, update pipeline_tag and link to paper
#1
by nielsr HF Staff - opened
Hi! I'm Niels from the community science team at Hugging Face.
This PR improves the model card for Mursit-Base by:
- Adding
library_name: transformersto the metadata to enable the code usage widget. - Updating the
pipeline_tagtofeature-extractionto reflect the model's primary use case in legal retrieval and embedding tasks as highlighted in the paper. - Adding a link to the original research paper.
These changes help improve the discoverability and usability of the model on the Hugging Face Hub.
nmmursit changed pull request status to merged