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
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library_name: transformers
pipeline_tag: summarization
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# Trained Text Summerizer BART:
- This model is fine-tuned version of the **facebook/bart-large-cnn** model.
- **TrainedTextSummerizerBART** is a model trained on **SAMSum** dataset from Hugging Face for summarization tasks.
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