Summarization
Transformers
Safetensors
English
t5
text2text-generation
text-2-text
natural-language
nlp
classification
call center
IT
text-generation
text-generation-inference
Instructions to use KameronB/sitcc-t5-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KameronB/sitcc-t5-classifier 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="KameronB/sitcc-t5-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("KameronB/sitcc-t5-classifier") model = AutoModelForSeq2SeqLM.from_pretrained("KameronB/sitcc-t5-classifier") - Notebooks
- Google Colab
- Kaggle
Ctrl+K
The sample code above would raise an error if the model returned improperly formatted responses before. Now it will show the actual response so you can see why re failed to parse it and it will return an object that is {'Software/System': 'Unknown', 'Issue/Request': 'Unknown'} instead of an error.
6a211f8 verified