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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from KameronB/sitcc-t5-classifier: direct link, hf CLI and curl.
- Browser
- Download file 4.66 kB
-
https://huggingface.co/KameronB/sitcc-t5-classifier/resolve/main/training_args.bin
- Command line
-
hf download hf://KameronB/sitcc-t5-classifier/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/KameronB/sitcc-t5-classifier/resolve/main/training_args.bin
4.66 kB
- Xet hash:
- 9e2336bc51da0ec7333c6940169072d75ef8f9ef149549b366b8084ae6e56eff
- Size of remote file:
- 4.66 kB
- SHA256:
- 5f2b843ce8ecfb34380bbaf515687a57abe0c9530b6ba0cd952c9c2592178153
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