Summarization
Transformers
Safetensors
English
multilingual
mbart
text2text-generation
seq2seq
extractive-summarization
conclusion-extraction
Instructions to use XiaHan19/mbart-large-50-extractive-conclusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use XiaHan19/mbart-large-50-extractive-conclusion 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="XiaHan19/mbart-large-50-extractive-conclusion")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("XiaHan19/mbart-large-50-extractive-conclusion") model = AutoModelForSeq2SeqLM.from_pretrained("XiaHan19/mbart-large-50-extractive-conclusion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 578e5359f28b5a33e47e8f431c66d8436f966b5b696b41b09e451b7ad9552b42
- Size of remote file:
- 15.4 kB
- SHA256:
- d0a64fde923a29ac1740c096461f01a9e631a76d53d2a86ea46753df2cf605b8
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