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 generation_config.json from KameronB/sitcc-t5-classifier: direct link, hf CLI and curl.
- Browser
- Download file 142 Bytes
-
https://huggingface.co/KameronB/sitcc-t5-classifier/resolve/main/generation_config.json
- Command line
-
hf download hf://KameronB/sitcc-t5-classifier/generation_config.json
-
curl -L -o generation_config.json https://huggingface.co/KameronB/sitcc-t5-classifier/resolve/main/generation_config.json
142 Bytes
| { | |
| "_from_model_config": true, | |
| "decoder_start_token_id": 0, | |
| "eos_token_id": 1, | |
| "pad_token_id": 0, | |
| "transformers_version": "4.37.2" | |
| } | |