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-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use newmindai/Mursit-Large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="newmindai/Mursit-Large")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("newmindai/Mursit-Large") model = AutoModelForMaskedLM.from_pretrained("newmindai/Mursit-Large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fe8c9718b0e7c14ec9879b6729378f285b6638f152272f9c8502495a1c29da8e
- Size of remote file:
- 1.62 GB
- SHA256:
- 37177cc0b0d406fbcdc2c6120a56410b1889c06fcd2c9379cc016e1935e1228f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.