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 pytorch_model.bin 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/pytorch_model.bin
- Command line
-
hf download hf://MaCoCu/XLMR-MaCoCu-tr/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/MaCoCu/XLMR-MaCoCu-tr/resolve/main/pytorch_model.bin
2.24 GB
- Xet hash:
- cc7fd682e521471539bdd13b1ed883d71af92615743527e66c5186b26b968b9e
- Size of remote file:
- 2.24 GB
- SHA256:
- d05ac03b6d837a3ce4c5bbc766ae738a755591a51e49ecb112d4afc482104edd
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