Instructions to use yeye776/t5-large-finetuned-multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yeye776/t5-large-finetuned-multi with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("yeye776/t5-large-finetuned-multi") model = AutoModelForSeq2SeqLM.from_pretrained("yeye776/t5-large-finetuned-multi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: cc-by-4.0
base_model: paust/pko-t5-large
tags:
- generated_from_trainer
model-index:
- name: t5-large-finetuned-multi
results: []
widget:
- text: 내일 용인에서 상차하고 다시 용인에서 하차하는 화물 추천해줘
example_title: 화물추천
- text: 내일 오후 우면동 날씨
example_title: 날씨예보
- text: 전기충전소 안내해줘
example_title: 장소안내
- text: 경부고속도로 상황 알려줘
example_title: 일상대화
- text: 하차 담당자에게 문의해줘
example_title: 전화연결
- text: 진행해줘
example_title: 긍부정
t5-large-finetuned-multi
This model is a fine-tuned version of paust/pko-t5-large on the None dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0007
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 8
Training results
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1