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