Summarization
Transformers
PyTorch
English
t5
text2text-generation
hupd
conditional-generation
patents
text-generation-inference
Instructions to use HUPD/hupd-t5-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HUPD/hupd-t5-small 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="HUPD/hupd-t5-small")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("HUPD/hupd-t5-small") model = AutoModelForSeq2SeqLM.from_pretrained("HUPD/hupd-t5-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4d213fca0e4e84447f9e4542fee06d88154bd42af321b26beaf2802e415ef7b2
- Size of remote file:
- 243 MB
- SHA256:
- 935387988c968a20fa0e8c2bc9ef16f7f8dbc3920d560e5a97f220f86c8eaf04
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