Instructions to use madlag/albert-base-v2-squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use madlag/albert-base-v2-squad 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="madlag/albert-base-v2-squad")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("madlag/albert-base-v2-squad") model = AutoModelForQuestionAnswering.from_pretrained("madlag/albert-base-v2-squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model_args.json from madlag/albert-base-v2-squad: direct link, hf CLI and curl.
- Browser
- Download file 166 Bytes
-
https://huggingface.co/madlag/albert-base-v2-squad/resolve/main/model_args.json
- Command line
-
hf download hf://madlag/albert-base-v2-squad/model_args.json
-
curl -L -o model_args.json https://huggingface.co/madlag/albert-base-v2-squad/resolve/main/model_args.json
166 Bytes
| { | |
| "cache_dir": null, | |
| "config_name": null, | |
| "model_name_or_path": "albert-base-v2", | |
| "tokenizer_name": "albert-base-v2", | |
| "use_fast_tokenizer": true | |
| } |