Instructions to use VRLLab/TurkishBERTweet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VRLLab/TurkishBERTweet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="VRLLab/TurkishBERTweet")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("VRLLab/TurkishBERTweet") model = AutoModelForMaskedLM.from_pretrained("VRLLab/TurkishBERTweet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from VRLLab/TurkishBERTweet: direct link, hf CLI and curl.
- Browser
- Download file 652 MB
-
https://huggingface.co/VRLLab/TurkishBERTweet/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://VRLLab/TurkishBERTweet/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/VRLLab/TurkishBERTweet/resolve/main/flax_model.msgpack
652 MB
- Xet hash:
- 0cff83130fcf48cf10970570c8c6abd998c5a50848f5690cb8a93eeb0eb6fb64
- Size of remote file:
- 652 MB
- SHA256:
- 946f884245a04072c90b1373b5d3bda7bc873221278dad9bb916ce0106b2ede4
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