Instructions to use mateiaass/albert-base-qa-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mateiaass/albert-base-qa-3 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="mateiaass/albert-base-qa-3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("mateiaass/albert-base-qa-3") model = AutoModelForQuestionAnswering.from_pretrained("mateiaass/albert-base-qa-3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mateiaass/albert-base-qa-3: direct link, hf CLI and curl.
- Browser
- Download file 44.4 MB
-
https://huggingface.co/mateiaass/albert-base-qa-3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mateiaass/albert-base-qa-3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mateiaass/albert-base-qa-3/resolve/main/pytorch_model.bin
44.4 MB
- Xet hash:
- ec5001721991cfadb226209023228963dc1c014f4b0db953c3321dbc8a02f139
- Size of remote file:
- 44.4 MB
- SHA256:
- 57d002bc91d146aea222bafd5eac7277208abaa6fba22cf26eba622474bcc82c
路
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