Summarization
Transformers
PyTorch
Core ML
ONNX
Safetensors
English
t5
text2text-generation
text-generation-inference
Instructions to use Falconsai/text_summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Falconsai/text_summarization 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="Falconsai/text_summarization")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Falconsai/text_summarization") model = AutoModelForSeq2SeqLM.from_pretrained("Falconsai/text_summarization") - Inference
- Notebooks
- Google Colab
- Kaggle
Questions regarding using this model on an Android app.
#12
by hm6502163 - opened
Hi guys,
I converted the model to TF Lite model to run on Android. When I get the output from the model, the shape is [1, 8, 1, 64]. Could someone help me understand how to decode it to the summarization text? If there is other advice about using this model on an Android app, please let me know. Thanks!
Best,
HanSolo
Apologies, the models are available as is, we have not leveraged the TFLite framework so we cannot guide you in this effort.
RealFalconsAI changed discussion status to closed