Instructions to use emrecan/bert-base-multilingual-cased-multinli_tr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emrecan/bert-base-multilingual-cased-multinli_tr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="emrecan/bert-base-multilingual-cased-multinli_tr")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("emrecan/bert-base-multilingual-cased-multinli_tr") model = AutoModelForSequenceClassification.from_pretrained("emrecan/bert-base-multilingual-cased-multinli_tr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from emrecan/bert-base-multilingual-cased-multinli_tr: direct link, hf CLI and curl.
- Browser
- Download file 712 MB
-
https://huggingface.co/emrecan/bert-base-multilingual-cased-multinli_tr/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://emrecan/bert-base-multilingual-cased-multinli_tr/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/emrecan/bert-base-multilingual-cased-multinli_tr/resolve/main/pytorch_model.bin
712 MB
- Xet hash:
- 159166b06b20725793c881586523908abfe2a5199ae292c3088437ce78d5305a
- Size of remote file:
- 712 MB
- SHA256:
- cb8c5fd0e524e2d655f466204e81b7df29ba8f2996cf72f4d4794165917acd74
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