Instructions to use Eraly-ml/KazBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Eraly-ml/KazBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Eraly-ml/KazBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Eraly-ml/KazBERT") model = AutoModelForMaskedLM.from_pretrained("Eraly-ml/KazBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download dev_dataset.json from Eraly-ml/KazBERT: direct link, hf CLI and curl.
- Browser
- Download file 234 MB
-
https://huggingface.co/Eraly-ml/KazBERT/resolve/main/dev_dataset.json
- Command line
-
hf download hf://Eraly-ml/KazBERT/dev_dataset.json
-
curl -L -o dev_dataset.json https://huggingface.co/Eraly-ml/KazBERT/resolve/main/dev_dataset.json
234 MB
- Xet hash:
- 99c99a8e3b0796e22a6416f486694999cbd32edbcdd24b9aaed3883b642b415f
- Size of remote file:
- 234 MB
- SHA256:
- 128c5b5299602e442370d26e80f85cd3a5a5ced47554720f7cdce3da8218a45c
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