Instructions to use LucasS/albertABSA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LucasS/albertABSA 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="LucasS/albertABSA")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("LucasS/albertABSA") model = AutoModelForQuestionAnswering.from_pretrained("LucasS/albertABSA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from LucasS/albertABSA: direct link, hf CLI and curl.
- Browser
- Download file 44.4 MB
-
https://huggingface.co/LucasS/albertABSA/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://LucasS/albertABSA/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/LucasS/albertABSA/resolve/main/pytorch_model.bin
44.4 MB
- Xet hash:
- 1bceb9dcc497cbfd48c411fa4388fb3e88b2d571e68a4bb1f686bedd480e4569
- Size of remote file:
- 44.4 MB
- SHA256:
- 377d2e43284a5d10423909508f49a8b304a44b2bbfaf371dac410b347cf8b67a
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