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:
- 1f92606b9b9be83b59dfd7ea2d8fe702fd5b430604f5d16e8d526874e6b6b18b
- Size of remote file:
- 15.4 kB
- SHA256:
- 587fdcf3ae06985b88deacc236980d81f2054497e75577b4f7ba00593654b08f
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