Instructions to use suchirsalhan/JA_BabyBERTa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use suchirsalhan/JA_BabyBERTa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="suchirsalhan/JA_BabyBERTa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("suchirsalhan/JA_BabyBERTa") model = AutoModel.from_pretrained("suchirsalhan/JA_BabyBERTa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from suchirsalhan/JA_BabyBERTa: direct link, hf CLI and curl.
- Browser
- Download file 34.3 MB
-
https://huggingface.co/suchirsalhan/JA_BabyBERTa/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://suchirsalhan/JA_BabyBERTa/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/suchirsalhan/JA_BabyBERTa/resolve/main/pytorch_model.bin
34.3 MB
- Xet hash:
- d567a50a71b1722858b13186e754f4bf39381786418e8e4bafc4ae8b1dba6d48
- Size of remote file:
- 34.3 MB
- SHA256:
- 2a76c96f4517d10ad0009ad316889316e4f6a0fdedf3b59f9a90b0950ff91c6d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.