Summarization
Transformers
PyTorch
TensorBoard
bart
text2text-generation
Generated from Trainer
Eval Results (legacy)
Instructions to use cs608/billsum-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cs608/billsum-model 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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("cs608/billsum-model") model = AutoModelForSeq2SeqLM.from_pretrained("cs608/billsum-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from cs608/billsum-model: direct link, hf CLI and curl.
- Browser
- Download file 3.77 kB
-
https://huggingface.co/cs608/billsum-model/resolve/main/training_args.bin
- Command line
-
hf download hf://cs608/billsum-model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cs608/billsum-model/resolve/main/training_args.bin
3.77 kB
- Xet hash:
- dfae84c8354ceddfe26448ed8babd3f7833e6440c9ac846f9695cb681c8e02ed
- Size of remote file:
- 3.77 kB
- SHA256:
- bfe5bf8caf92e89e0b2934470d04609efc487bf2b7740de49ae495ba2df2c77f
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