Instructions to use roykim/Summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use roykim/Summarization with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("roykim/Summarization") model = AutoModelForSeq2SeqLM.from_pretrained("roykim/Summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 352 Bytes
59e423f f63bba1 59e423f | 1 2 3 4 5 6 7 8 | {
"model_max_length": 1000000000000000019884624838656,
"name_or_path": "gogamza/kobart-summarization",
"special_tokens_map_file": "/opt/ml/.cache/huggingface/hub/models--gogamza--kobart-summarization/snapshots/8a63d6913edc0e16a902e3fa8b688a134f0dd776/special_tokens_map.json",
"tokenizer_class": "PreTrainedTokenizerFast",
"use_fast": true
}
|