Instructions to use Kdogs/topic_IT-Science_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kdogs/topic_IT-Science_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Kdogs/topic_IT-Science_bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Kdogs/topic_IT-Science_bert") model = AutoModelForMaskedLM.from_pretrained("Kdogs/topic_IT-Science_bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Kdogs/topic_IT-Science_bert: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/Kdogs/topic_IT-Science_bert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Kdogs/topic_IT-Science_bert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Kdogs/topic_IT-Science_bert/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- 0ac19fd0a033cb29b6ab5b263607c8697e1c1a3b3e149db1afdf16e44783d283
- Size of remote file:
- 443 MB
- SHA256:
- 3d3269574954fb3d894e68d38687d222d25c4563653f88d8eb88a24c62550959
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