Summarization
Transformers
PyTorch
TensorFlow
Safetensors
English
pegasus
text2text-generation
Eval Results (legacy)
Instructions to use human-centered-summarization/financial-summarization-pegasus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use human-centered-summarization/financial-summarization-pegasus 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="human-centered-summarization/financial-summarization-pegasus")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("human-centered-summarization/financial-summarization-pegasus") model = AutoModelForSeq2SeqLM.from_pretrained("human-centered-summarization/financial-summarization-pegasus") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0046ff20cef09d5b91f5306adf919d897d10e16c785c5b5f09ab32998ddbab58
- Size of remote file:
- 2.28 GB
- SHA256:
- 118a4679c18c62b4b16518941b19ca5debaf9e8676b299f4d62c8bd450fdcb30
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