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-# Introduction
-
-This is the TripPy DST module for ConvLab-3.
-
-## Supported encoders
-
-* RoBERTa
-* BERT (full support w.i.p.)
-* ELECTRA (full support w.i.p.)
-
-## Supported datasets
-
-* MultiWOZ 2.X
-* Unified Data Format
-
-## Requirements
-
-transformers (tested: 4.18.0)
-torch (tested: 1.8.0)
-
-# Parameters
-
-```
-model_type # Default: "roberta", Type of the model (Supported: "roberta", "bert", "electra")
-model_name # Default: "roberta-base", Name of the model (Use -h to print a list of names)
-model_path # Path to a model checkpoint
-dataset_name # Default: "multiwoz21", Name of the dataset the model was trained on and/or is being applied to
-local_files_only # Default: False, Set to True to load local files only. Useful for offline systems 
-nlu_usr_config # Path to a NLU config file. Only needed for internal evaluation
-nlu_sys_config # Path to a NLU config file. Only needed for internal evaluation
-nlu_usr_path # Path to a NLU model file. Only needed for internal evaluation
-nlu_sys_path # Path to a NLU model file. Only needed for internal evaluation
-no_eval # Default: True, Set to True if internal evaluation should be conducted
-no_history # Default: False, Set to True if dialogue history should be omitted during inference
-```
-
-# Training
-
-TripPy can easily be trained for the abovementioned supported datasets using the original code in the official [TripPy repository](https://gitlab.cs.uni-duesseldorf.de/general/dsml/trippy-public). Simply clone the code and run the appropriate DO.* script to train a TripPy DST. After training, set model_path to the preferred checkpoint to use TripPy in ConvLab-3.
-
-# Training and evaluation with PPO policy
-
-Switch to the directory:
-```
-cd ../../policy/ppo
-```
-
-Edit trippy_config.json and trippy_config_eval.json accordingly, e.g., edit paths to model checkpoints.
-
-For training, run
-```
-train.py --path trippy_config.json
-```
-
-For evaluation, run
-```
-train.py --path trippy_config_eval.json
-```
-
-# Paper
-
-[TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking](https://aclanthology.org/2020.sigdial-1.4/)