Feature Extraction
Transformers
PyTorch
TensorFlow
JAX
Turkish
xlm-roberta
MaCoCu
text-embeddings-inference
Instructions to use MaCoCu/XLMR-MaCoCu-tr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MaCoCu/XLMR-MaCoCu-tr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MaCoCu/XLMR-MaCoCu-tr")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("MaCoCu/XLMR-MaCoCu-tr") model = AutoModel.from_pretrained("MaCoCu/XLMR-MaCoCu-tr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from MaCoCu/XLMR-MaCoCu-tr: direct link, hf CLI and curl.
- Browser
- Download file 2.24 GB
-
https://huggingface.co/MaCoCu/XLMR-MaCoCu-tr/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://MaCoCu/XLMR-MaCoCu-tr/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/MaCoCu/XLMR-MaCoCu-tr/resolve/main/flax_model.msgpack
2.24 GB
- Xet hash:
- 7fab8686619f7fff6c688cccd569ba5cf88a1fbfd8c9df359881bba0a7a359b0
- Size of remote file:
- 2.24 GB
- SHA256:
- 2f006d08fa1232184dc045c59f11f85db407168eabd5ffb834035b58fa142236
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