Summarization
Transformers
Safetensors
Ukrainian
English
t5
text2text-generation
text-generation-inference
Instructions to use d0p3/O3ap-sm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use d0p3/O3ap-sm 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="d0p3/O3ap-sm")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("d0p3/O3ap-sm") model = AutoModelForSeq2SeqLM.from_pretrained("d0p3/O3ap-sm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d6a1f8076eb9292e57fc9d827d137b14f89980c1f82d078fd114cb9c69e8b928
- Size of remote file:
- 4.6 kB
- SHA256:
- 99c50abc7985a3ff920619c2877c2cf8d5ce42d73741e6d85b27e924d84a731f
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