Instructions to use nirupamadasari/bartlfqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nirupamadasari/bartlfqa 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="nirupamadasari/bartlfqa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("nirupamadasari/bartlfqa") model = AutoModelForQuestionAnswering.from_pretrained("nirupamadasari/bartlfqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from nirupamadasari/bartlfqa: direct link, hf CLI and curl.
- Browser
- Download file 1.63 GB
-
https://huggingface.co/nirupamadasari/bartlfqa/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://nirupamadasari/bartlfqa/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nirupamadasari/bartlfqa/resolve/main/pytorch_model.bin
1.63 GB
- Xet hash:
- 0f694b36da43bcd093729e30b863647e11af941a62820f85de036162a0a54686
- Size of remote file:
- 1.63 GB
- SHA256:
- aa69f6f77e1af6c0182026b57ffb7f9c213c071ff2bd77dc590d3ecef9a4766f
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.