Instructions to use Violeta/ArmBERTa_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Violeta/ArmBERTa_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Violeta/ArmBERTa_Model")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Violeta/ArmBERTa_Model") model = AutoModel.from_pretrained("Violeta/ArmBERTa_Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Violeta/ArmBERTa_Model: direct link, hf CLI and curl.
- Browser
- Download file 442 MB
-
https://huggingface.co/Violeta/ArmBERTa_Model/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Violeta/ArmBERTa_Model/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Violeta/ArmBERTa_Model/resolve/main/flax_model.msgpack
442 MB
- Xet hash:
- 9eab009716d47ec9f757c67a980a6be9086a7d94d6c98b59ef491d1af5d2ae30
- Size of remote file:
- 442 MB
- SHA256:
- 89001d641c5969dfbfaed87438d3b44ee191a77a5fb31765fe8010201b501da5
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