Summarization
Transformers
ONNX
Safetensors
English
t5
text2text-generation
text-summarization
meeting-summarization
qmsum
text-generation-inference
Instructions to use CodeXRyu/meeting-summarizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CodeXRyu/meeting-summarizer 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="CodeXRyu/meeting-summarizer")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("CodeXRyu/meeting-summarizer") model = AutoModelForSeq2SeqLM.from_pretrained("CodeXRyu/meeting-summarizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8a558b791fd09569c62ae6f1b7f58ef0f5f55c597a336addb939f5d39f79e534
- Size of remote file:
- 484 MB
- SHA256:
- d8780b109dfc27662eb489f645bfc3ad832cc24826668c1a8f9e819f58db915f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.