Instructions to use madlag/bert-base-uncased-squad1.1-block-sparse-0.20-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use madlag/bert-base-uncased-squad1.1-block-sparse-0.20-v1 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/bert-base-uncased-squad1.1-block-sparse-0.20-v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("madlag/bert-base-uncased-squad1.1-block-sparse-0.20-v1") model = AutoModelForQuestionAnswering.from_pretrained("madlag/bert-base-uncased-squad1.1-block-sparse-0.20-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
bert-base-uncased-squad1.1-block-sparse-0.20-v1 / model_card /layer_images /layer_2_attention_self_query.png
Download model_card/layer_images/layer_2_attention_self_query.png from madlag/bert-base-uncased-squad1.1-block-sparse-0.20-v1: direct link, hf CLI and curl.
- Browser
- Download file 4.56 kB
-
https://huggingface.co/madlag/bert-base-uncased-squad1.1-block-sparse-0.20-v1/resolve/979839bb823eff87bfa50f324d8f73fff8a57731/model_card/layer_images/layer_2_attention_self_query.png
- Command line
-
hf download hf://madlag/bert-base-uncased-squad1.1-block-sparse-0.20-v1@979839bb823eff87bfa50f324d8f73fff8a57731/model_card/layer_images/layer_2_attention_self_query.png
-
curl -L -o layer_2_attention_self_query.png https://huggingface.co/madlag/bert-base-uncased-squad1.1-block-sparse-0.20-v1/resolve/979839bb823eff87bfa50f324d8f73fff8a57731/model_card/layer_images/layer_2_attention_self_query.png
4.56 kB
