t5-small-nlu-multiwoz21-context3
This model is a fine-tuned version of t5-small on MultiWOZ 2.1 with context window size == 3.
Refer to ConvLab-3 for model description and usage.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 128
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 10.0
Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
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Dataset used to train ConvLab/t5-small-nlu-multiwoz21-context3
Evaluation results
- Accuracy on MultiWOZ 2.1test set self-reported82.000
- F1 on MultiWOZ 2.1test set self-reported90.300
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ConvLab/t5-small-nlu-multiwoz21-context3") model = AutoModelForSeq2SeqLM.from_pretrained("ConvLab/t5-small-nlu-multiwoz21-context3")