Instructions to use HasinMDG/T5-base-Topics-Summarizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HasinMDG/T5-base-Topics-Summarizer with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("HasinMDG/T5-base-Topics-Summarizer") model = AutoModelForMultimodalLM.from_pretrained("HasinMDG/T5-base-Topics-Summarizer") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5f97e0d222e8e384dbe2c154ddace27f2ccbccbdab6db249add20e5b9b1dca51
- Size of remote file:
- 892 MB
- SHA256:
- 5a7c0c5918eeb6db7d82a4053198170d2ce3a39780dae738ff6c782e6e167a00
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