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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7249f080aaeed1a0d19104cee1d2a87b2e730656ed3b5921b47d7605a871296a
- Size of remote file:
- 1.63 GB
- SHA256:
- cfa352cc81ad4fc8330bc86104609239650a3096514a391f9ab046dbe08a9a93
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.