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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("newmindai/Mursit-Base") model = AutoModelForMaskedLM.from_pretrained("newmindai/Mursit-Base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add library_name, update pipeline_tag and link to paper
Verified signatureHi! I'm Niels from the community science team at Hugging Face. This PR improves the model card for Mursit-Base by: - Adding `library_name: transformers` to the metadata to enable the code usage widget. - Updating the `pipeline_tag` to `feature-extraction` to 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.b88caaf
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Update README.md
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Initial model upload - clean repository
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initial commit
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