Instructions to use alfandy/bert2bert-batch2-lr1e-5-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alfandy/bert2bert-batch2-lr1e-5-summarization with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("alfandy/bert2bert-batch2-lr1e-5-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("alfandy/bert2bert-batch2-lr1e-5-summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a4562a60099dd5050f1605a2b6cecb863f018e8122274d80e40c81719ec2bc67
- Size of remote file:
- 4.98 kB
- SHA256:
- 3b81502068096c703930c5718565553b5d6d98550031a38775ecfe9c091e1646
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