Summarization
Transformers
TensorBoard
Safetensors
English
bart
text2text-generation
summarizer
text summarization
abstractive summarization
Instructions to use KipperDev/bart_summarizer_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KipperDev/bart_summarizer_model 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="KipperDev/bart_summarizer_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("KipperDev/bart_summarizer_model") model = AutoModelForSeq2SeqLM.from_pretrained("KipperDev/bart_summarizer_model") - Notebooks
- Google Colab
- Kaggle
bart_summarizer_model / runs /Feb08_17-39-21_4b007708813c /events.out.tfevents.1707414174.4b007708813c.408.0
- Xet hash:
- 7036ab8be3023844aa72442d7748c364b1187684350684922c412bd4732ee00a
- Size of remote file:
- 11.2 kB
- SHA256:
- 8b0a820e5d352a38e23a84c35e0c0c611edfe84a1612e0481ec7a2de3ddf706d
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