Update Trippy README links (#94)

* Seperate test and train domains

* Add progress bars in ontology embedder

* Update custom_util.py

* Fix custom_util things I broke

* Github master

* Save dialogue ids in prediction file

* Fix bug in ontology enxtraction

* Return dialogue ids in predictions file and fix bugs

* Add setsumbt starting config loader

* Add script to extract golden labels from dataset to match model predictions

* Add more setsumbt configs

* Add option to use local files only in transformers package

* Update starting configurations for setsumbt

* Github master

* Update README.md

* Update README.md

Co-authored-by: Carel van Niekerk <carel.niekerk@hhu.de>
Co-authored-by: Michael Heck <michael.heck@hhu.de>

convlab/dst/trippy/README.md

@@ -15,25 +15,45 @@ This is the TripPy DST module for ConvLab-3.
 
 ## Requirements
 
-transformers (tested: 4.18.0)
-torch (tested: 1.8.0)
+* 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
+model_path # Path to a model checkpoint. Note, this can also be a HuggingFace model
 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_sys_config # Path to a NLU config file. Only needed when using word-level policies
 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
+nlu_sys_path # Path to a NLU model file. Only needed when using word-level policies
+no_eval # Default: True, Set to False if internal evaluation should be conducted
 no_history # Default: False, Set to True if dialogue history should be omitted during inference
 ```
 
+# Model checkpoint
+
+A model checkpoint can either be trained from scratch using the TripPy codebase (see below), or a ready-to-use checkpoint can be loaded from the [HuggingFace repository](https://huggingface.co/ConvLab) for ConvLab.
+
+Currently, the following checkpoint is available to be loaded from HuggingFace:
+
+```
+ConvLab/roberta-base-trippy-dst-multiwoz21
+```
+
+To load this checkpoint, use the following parameters for TripPy DST in ConvLab-3:
+
+```
+model_type="roberta"
+model_name="roberta-base"
+model_path="ConvLab/roberta-base-trippy-dst-multiwoz21"
+```
+
+The checkpoint will be downloaded and cached automatically.
+
 # 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.
@@ -45,7 +65,7 @@ Switch to the directory:
 cd ../../policy/ppo
 ```
 
-Edit trippy_config.json and trippy_config_eval.json accordingly, e.g., edit paths to model checkpoints.
+Edit trippy_config.json accordingly, e.g., edit paths to model checkpoints.
 
 For training, run
 ```

convlab/policy/ppo/semantic_level_config.json

@@ -6,7 +6,7 @@
 		"batchsz": 1000,
 		"seed": 0,
 		"epoch": 10,
-		"eval_frequency": 1,
+		"eval_frequency": 5,
 		"process_num": 4,
 		"sys_semantic_to_usr": false,
 		"num_eval_dialogues": 500