Instructions to use knkarthick/MEETING-SUMMARY-BART-LARGE-XSUM-SAMSUM-DIALOGSUM-AMI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use knkarthick/MEETING-SUMMARY-BART-LARGE-XSUM-SAMSUM-DIALOGSUM-AMI 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-BART-LARGE-XSUM-SAMSUM-DIALOGSUM-AMI")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("knkarthick/MEETING-SUMMARY-BART-LARGE-XSUM-SAMSUM-DIALOGSUM-AMI") model = AutoModelForSeq2SeqLM.from_pretrained("knkarthick/MEETING-SUMMARY-BART-LARGE-XSUM-SAMSUM-DIALOGSUM-AMI", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Short Summaries
#7
by shortsnip - opened
I've found this model also works really well at summarizing shorter documents (100-500 characters). However every now and then something like this happens:
Input: Republicans Are Blaming Biden For A Border Crisis That Republicans Refuse To Fix
Result: Biden is Blaming Biden for a Border Crisis.
Kinda funny but curious if there's anything I can do configuration wise to reduce these kind of occurrences. Thank you!
This model was trained on conversational data.
From your example you should look for models trained to summarize news articles or non-dialogue style texts.
knkarthick changed discussion status to closed