Summarization
Transformers
PyTorch
Safetensors
English
bart
text2text-generation
seq2seq
Eval Results (legacy)
Instructions to use lidiya/bart-base-samsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lidiya/bart-base-samsum 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="lidiya/bart-base-samsum")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("lidiya/bart-base-samsum") model = AutoModelForSeq2SeqLM.from_pretrained("lidiya/bart-base-samsum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7a973e89769f7fcb979ef92ad7f17b04422b70857a32a46d706b588be87b1985
- Size of remote file:
- 558 MB
- SHA256:
- 3add531fd0465ffeb669bd1b36dc8371833019f6e7a0eb36cc58c0dfc6ee20a6
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