Summarization
Transformers
PyTorch
English
t5
text2text-generation
wikihow
t5-small
lm-head
seq2seq
pipeline:summarization
text-generation-inference
Instructions to use deep-learning-analytics/wikihow-t5-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deep-learning-analytics/wikihow-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="deep-learning-analytics/wikihow-t5-small")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("deep-learning-analytics/wikihow-t5-small") model = AutoModelForSeq2SeqLM.from_pretrained("deep-learning-analytics/wikihow-t5-small") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cca866c26bb1b8b01deb784fc342407773462db0e666d3ee96048d8b60661ac9
- Size of remote file:
- 242 MB
- SHA256:
- 9374d3f2ab7f758b7a00ea70b1ac2608aac13c67d37a7131987cc221b867dc23
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