Instructions to use twmkn9/albert-base-v2-squad2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use twmkn9/albert-base-v2-squad2 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="twmkn9/albert-base-v2-squad2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("twmkn9/albert-base-v2-squad2") model = AutoModelForQuestionAnswering.from_pretrained("twmkn9/albert-base-v2-squad2", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download training_args.bin from twmkn9/albert-base-v2-squad2: direct link, hf CLI and curl.
- Browser
- Download file 1.46 kB
-
https://huggingface.co/twmkn9/albert-base-v2-squad2/resolve/main/training_args.bin
- Command line
-
hf download hf://twmkn9/albert-base-v2-squad2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/twmkn9/albert-base-v2-squad2/resolve/main/training_args.bin
1.46 kB
- Xet hash:
- 7dffde89e5ddabae188d1ab39415c847073a4d760e14e2b0d48529ced0b38baf
- Size of remote file:
- 1.46 kB
- SHA256:
- d3f79dcebb10d454bedfd8143e736489f55e0a8097cd77468dbaa9ee1d282fba
路
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