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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from newmindai/Mursit-Base: direct link, hf CLI and curl.
- Browser
- Download file 625 MB
-
https://huggingface.co/newmindai/Mursit-Base/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://newmindai/Mursit-Base@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/newmindai/Mursit-Base/resolve/refs%2Fpr%2F1/pytorch_model.bin
625 MB
- Xet hash:
- bb1374113cd91c61fe65c8b02f1d2622580469936f6ebebe4597a12ab03ca77c
- Size of remote file:
- 625 MB
- SHA256:
- 2b0e5315d7a508cada6a90ebb1704ba8aca3e8cf3863656fd3789744ac2d8ccc
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