Summarization
Transformers
PyTorch
English
bart
text2text-generation
conversational
seq2seq
bart large
Eval Results (legacy)
Instructions to use yashugupta786/bart_large_xsum_samsum_conv_summarizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yashugupta786/bart_large_xsum_samsum_conv_summarizer 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="yashugupta786/bart_large_xsum_samsum_conv_summarizer") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("yashugupta786/bart_large_xsum_samsum_conv_summarizer") model = AutoModelForSeq2SeqLM.from_pretrained("yashugupta786/bart_large_xsum_samsum_conv_summarizer") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 891a34b01d1f389968e321279848de1b84a1a7f666b5a9cd52dcc5665dbeebc3
- Size of remote file:
- 1.63 GB
- SHA256:
- 23451d1c297e87281448cb206d294feb2f614cb30063e475ff40687260087c99
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