Instructions to use alfandy/bert2bert-batch2-lr5e-5-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alfandy/bert2bert-batch2-lr5e-5-summarization with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("alfandy/bert2bert-batch2-lr5e-5-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("alfandy/bert2bert-batch2-lr5e-5-summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7f97ad9461b211b37e3749e0e2b87a96041c33c94d9120d332682b0db8591212
- Size of remote file:
- 4.98 kB
- SHA256:
- 11f2e20868621f1bb8809ea0b5312b4b0928783c557350ff0573d335d24b0713
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