Instructions to use SRDdev/QABERT-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SRDdev/QABERT-small with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="SRDdev/QABERT-small")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("SRDdev/QABERT-small") model = AutoModelForQuestionAnswering.from_pretrained("SRDdev/QABERT-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from SRDdev/QABERT-small: direct link, hf CLI and curl.
- Browser
- Download file 265 MB
-
https://huggingface.co/SRDdev/QABERT-small/resolve/031343de007e8b5789ea798e9a8ff700afaa511a/pytorch_model.bin
- Command line
-
hf download hf://SRDdev/QABERT-small@031343de007e8b5789ea798e9a8ff700afaa511a/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/SRDdev/QABERT-small/resolve/031343de007e8b5789ea798e9a8ff700afaa511a/pytorch_model.bin
265 MB
- Xet hash:
- af2c921105ae4b93d6acd0771160f3ad29fc5c6c0fbd4aaf08b1c79ef7a2d14e
- Size of remote file:
- 265 MB
- SHA256:
- 9158014e99ae0a268d4174d495b047e2459d929a5111cc5ac3e03c42c2346050
路
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