Summarization
Transformers
PyTorch
TensorBoard
bart
text2text-generation
Generated from Trainer
Eval Results (legacy)
Instructions to use cs608/billsum-model-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cs608/billsum-model-2 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" 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("summarization", model="cs608/billsum-model-2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("cs608/billsum-model-2") model = AutoModelForSeq2SeqLM.from_pretrained("cs608/billsum-model-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from cs608/billsum-model-2: direct link, hf CLI and curl.
- Browser
- Download file 558 MB
-
https://huggingface.co/cs608/billsum-model-2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://cs608/billsum-model-2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/cs608/billsum-model-2/resolve/main/pytorch_model.bin
558 MB
- Xet hash:
- 18fa2098592f344ceb754f6cfe4030bee5c9572b89807dcaf4729a7969137687
- Size of remote file:
- 558 MB
- SHA256:
- fd9494ac06fe64f52bafee5c8599a6861b1d1ec663b261f1b1990e74b62b5f41
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.