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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 42d30507698988e305f1304e52c5b7a560a99b61a580275204ffaaf441b4b572
- Size of remote file:
- 242 MB
- SHA256:
- 6c61fc68ab29be6926a4982a5e04d5e3c461c5a69e5aeefa9e6dc1c0cc946084
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