Summarization
Transformers
PyTorch
TensorFlow
Safetensors
English
bart
text2text-generation
seq2seq
Eval Results (legacy)
Instructions to use knkarthick/MEETING_SUMMARY with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use knkarthick/MEETING_SUMMARY 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="knkarthick/MEETING_SUMMARY")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("knkarthick/MEETING_SUMMARY") model = AutoModelForSeq2SeqLM.from_pretrained("knkarthick/MEETING_SUMMARY") - Inference
- Notebooks
- Google Colab
- Kaggle
Increase Summary Length
#2
by jseeburger - opened
Hello,
Is it possible to increase the length of the summary produced by the model?
It has to be retuned for that to increase the size beyond say 128 tokens!
Would you please tell us how we can retune the model for increasing the length of the summary produced?
You will have to change the architecture of the model and change the training target size too and retrain/ retune the model to change it as per the desired summary length. This model has 1:10 compression.
knkarthick changed discussion status to closed