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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7cf9aab59c14642646c3f8772026a655fd8d3c3bff267a33dda052a54593e9bb
- Size of remote file:
- 1.63 GB
- SHA256:
- b3d57df761cf68f2c6af6c7b25869b2864a9e1ab0a34dbefe6a9af7e15bfce6d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.