Instructions to use Xmm/bart-large-entity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Xmm/bart-large-entity 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="Xmm/bart-large-entity")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Xmm/bart-large-entity") model = AutoModelForSeq2SeqLM.from_pretrained("Xmm/bart-large-entity", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 41cc1ae762f0c4e691991f9b3066f8e97d70f256191c7cd97fd8fed653697879
- Size of remote file:
- 3.25 GB
- SHA256:
- 3f08460f0dd4c4ad62a2b46b98822306be801197b10115e6050ac6341322a35e
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