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
Add evaluation results on the bazzhangz--sumdataset config and train split of bazzhangz/sumdataset
#3
by autoevaluator HF Staff - opened
Beep boop, I am a bot from Hugging Face's automatic model evaluator ๐!
Your model has been evaluated on the bazzhangz--sumdataset config and train split of the bazzhangz/sumdataset dataset by @bazzhangz , using the predictions stored here.
Accept this pull request to see the results displayed on the Hub leaderboard.
Evaluate your model on more datasets here.
knkarthick changed pull request status to merged