added model links to readme

convlab/policy/README.md

@@ -43,7 +43,7 @@ The necessary step before starting a training is to set up the environment and p
 ```
 {
 	"model": {
-		"load_path": "", # specify a loading path to load a pre-trained model 
+		"load_path": "", # specify a loading path to load a pre-trained model, omit the ending .pol.mdl
 		"use_pretrained_initialisation": false, # will download a provided ConvLab-3 model
 		"pretrained_load_path": "",
 		"seed": 0, # the seed for the experiment

convlab/policy/mle/README.md

@@ -14,6 +14,9 @@ The dataset name can be "multiwoz21" or "sgd" for instance. The first time you r
 
 Other hyperparameters such as learning rate or number of epochs can be set in the config.json file.
 
+We provide a model trained on multiwoz21 on hugging-face: https://huggingface.co/ConvLab/mle-policy-multiwoz21
+
+
 ## Evaluation
 
 Evaluation on the validation data set takes place during training.
\ No newline at end of file

convlab/policy/vector/vector_nodes.py

 # -*- coding: utf-8 -*-
 import sys
 import numpy as np
+import logging
+
 from convlab.util.multiwoz.lexicalize import delexicalize_da, flat_da
 from .vector_base import VectorBase
 
@@ -8,9 +10,11 @@ from .vector_base import VectorBase
 class VectorNodes(VectorBase):
 
     def __init__(self, dataset_name='multiwoz21', character='sys', use_masking=False, manually_add_entity_names=True,
-                 seed=0):
+                 seed=0, filter_state=True):
 
         super().__init__(dataset_name, character, use_masking, manually_add_entity_names, seed)
+        self.filter_state = filter_state
+        logging.info(f"We filter state by active domains: {self.filter_state}")
 
     def get_state_dim(self):
         self.belief_state_dim = 0
@@ -56,9 +60,16 @@ class VectorNodes(VectorBase):
         self.get_user_act_feature(state)
         self.get_sys_act_feature(state)
         domain_active_dict = self.get_user_goal_feature(state, domain_active_dict)
-        number_entities_dict = self.get_db_features()
         self.get_general_features(state, domain_active_dict)
 
+        if self.db is not None:
+            number_entities_dict = self.get_db_features()
+        else:
+            number_entities_dict = None
+
+        if self.filter_state:
+            self.kg_info = self.filter_inactive_domains(domain_active_dict)
+
         if self.use_mask:
             mask = self.get_mask(domain_active_dict, number_entities_dict)
             for i in range(self.da_dim):
@@ -89,6 +100,8 @@ class VectorNodes(VectorBase):
 
         feature_type = 'user goal'
         for domain in self.belief_domains:
+            # the if case is needed because SGD only saves the dialogue state info for active domains
+            if domain in state['belief_state']:
                 for slot, value in state['belief_state'][domain].items():
                     description = f"user goal-{domain}-{slot}".lower()
                     value = 1.0 if (value and value != "not mentioned") else 0.0
@@ -128,6 +141,7 @@ class VectorNodes(VectorBase):
     def get_general_features(self, state, domain_active_dict):
 
         feature_type = 'general'
+        if 'booked' in state:
             for i, domain in enumerate(self.db_domains):
                 if domain in state['booked']:
                     description = f"general-{domain}-booked".lower()
@@ -140,3 +154,17 @@ class VectorNodes(VectorBase):
             value = 1.0 if domain_active_dict[domain] else 0
             description = f"general-{domain}".lower()
             self.add_graph_node(domain, feature_type, description, value)
+
+    def filter_inactive_domains(self, domain_active_dict):
+
+        kg_filtered = []
+        for node in self.kg_info:
+            domain = node['domain']
+            if domain in domain_active_dict:
+                if domain_active_dict[domain]:
+                    kg_filtered.append(node)
+            else:
+                kg_filtered.append(node)
+
+        return kg_filtered
+

convlab/policy/vtrace_DPT/README.md

@@ -20,7 +20,11 @@ You can specify the dataset that you would like to use, e.g. "multiwoz21" or "sg
 
 You can specify hyperparamters such as epoch, supervised_lr and data_percentage (how much of the data you want to use) in the config.json file.
 
+We provide several supervised trained models on hugging-face to reproduce the results:
 
+- pre-trained on SGD: https://huggingface.co/ConvLab/ddpt-policy-sgd
+- pre-trained on 1% multiwoz21: https://huggingface.co/ConvLab/ddpt-policy-0.01multiwoz21
+- pre-trained on SGD and afterwards on 1% multiwoz21: https://huggingface.co/ConvLab/ddpt-policy-sgd_0.01multiwoz21
 
 ## RL training