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
- Xet hash:
- bb1374113cd91c61fe65c8b02f1d2622580469936f6ebebe4597a12ab03ca77c
- Size of remote file:
- 625 MB
- SHA256:
- 2b0e5315d7a508cada6a90ebb1704ba8aca3e8cf3863656fd3789744ac2d8ccc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.