Instructions to use songbo/dst_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use songbo/dst_model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("songbo/dst_model") model = AutoModelForSeq2SeqLM.from_pretrained("songbo/dst_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
base_model: google-t5/t5-small
tags:
- generated_from_trainer
model-index:
- name: dst_model
results: []
dst_model
This model is a fine-tuned version of google-t5/t5-small 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 5000
Training results
Framework versions
- Transformers 4.34.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.0
- Tokenizers 0.14.1