Summarization
Transformers
PyTorch
Safetensors
longt5
text2text-generation
long
title generation
Eval Results (legacy)
Instructions to use Joemgu/mlong-t5-large-sumstew with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Joemgu/mlong-t5-large-sumstew 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="Joemgu/mlong-t5-large-sumstew")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Joemgu/mlong-t5-large-sumstew") model = AutoModelForSeq2SeqLM.from_pretrained("Joemgu/mlong-t5-large-sumstew", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8e2b1bd499b59375382b0ea4120bce352078a58631de6d82e040931459891847
- Size of remote file:
- 4.97 GB
- SHA256:
- ca16eb2b14609961b1404483c03c7213a6e80e4d8df8d5ab915176526dbfb436
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