Instructions to use pratiknichite/TrainedTextSummerizerBART with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pratiknichite/TrainedTextSummerizerBART 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="pratiknichite/TrainedTextSummerizerBART")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("pratiknichite/TrainedTextSummerizerBART") model = AutoModelForSeq2SeqLM.from_pretrained("pratiknichite/TrainedTextSummerizerBART", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Update README.md
Verified signature190e888 -
Update README.md
Verified signature9aa802a -
Update README.md
Verified signature845fb89 -
Update README.md
Verified signaturea86aa89 -
Update README.md
Verified signature07b4263 -
Upload 9 files
Verified signaturea77af3d -
Delete bart_trained_model
Verified signature023fff1 -
Create README.md
Verified signature8386c60 -
Upload 9 files
Verified signatureaf51534 -
initial commit
Verified signature07c1af7