Release 0.6.0 (#989)

risesocket mode
Rasa wrapper
sentiment on ru conversational Bert
new gobot tutorial
minor fixes and documentation updates

.gitignore

@@ -109,6 +109,10 @@ ENV/
 #vscode IDE
 .vscode
 
+# Vim
+*.vim
+*.vimrc
+
 #GIT
 .git/
 

README.md

@@ -17,11 +17,13 @@ DeepPavlov is designed for
     * Contribution Guide [*docs:contribution_guide/*](http://docs.deeppavlov.ai/en/master/devguides/contribution_guide.html)
 * Issues [*github/issues/*](https://github.com/deepmipt/DeepPavlov/issues)
 * Forum [*forum.ipavlov.ai*](https://forum.ipavlov.ai/)
-* Blogs [*ipavlov.ai/#rec108281800*](http://ipavlov.ai/#rec108281800)
+* Blogs [*medium.com/deeppavlov*](https://medium.com/deeppavlov)
 * Tutorials [*examples/*](https://github.com/deepmipt/DeepPavlov/tree/master/examples) and [extended colab tutorials](https://github.com/deepmipt/dp_tutorials)
 * Docker Hub [*hub.docker.com/u/deeppavlov/*](https://hub.docker.com/u/deeppavlov/) 
     * Docker Images Documentation [*docs:docker-images/*](http://docs.deeppavlov.ai/en/master/intro/installation.html#docker-images)
 
+Please leave us [your feedback](https://forms.gle/i64fowQmiVhMMC7f9) on how we can improve the DeepPavlov framework.
+
 **Models**
 
 [Named Entity Recognition](http://docs.deeppavlov.ai/en/master/features/models/ner.html) | [Slot filling](http://docs.deeppavlov.ai/en/master/features/models/slot_filling.html)
@@ -54,6 +56,14 @@ DeepPavlov is designed for
 
 [Tuning Models with Evolutionary Algorithm](http://docs.deeppavlov.ai/en/master/features/hypersearch.html)
 
+**Integrations**
+
+[REST API](http://docs.deeppavlov.ai/en/master/integrations/rest_api.html) | [Socket API](http://docs.deeppavlov.ai/en/master/integrations/socket_api.html) | [Yandex Alice](http://docs.deeppavlov.ai/en/master/integrations/yandex_alice.html)
+
+[Telegram](http://docs.deeppavlov.ai/en/master/integrations/telegram.html) | [Microsoft Bot Framework](http://docs.deeppavlov.ai/en/master/integrations/ms_bot.html)
+
+[Amazon Alexa](http://docs.deeppavlov.ai/en/master/integrations/amazon_alexa.html) | [Amazon AWS](http://docs.deeppavlov.ai/en/master/integrations/aws_ec2.html)
+
 ## Installation
 
 0. We support `Linux` and `Windows` platforms, `Python 3.6` and `Python 3.7`
@@ -85,8 +95,8 @@ List of models is available on
 [the doc page](http://docs.deeppavlov.ai/en/master/features/overview.html) in
 the `deeppavlov.configs` (Python):
 
-```python 
-    from deeppavlov import configs
+```python
+from deeppavlov import configs
 ```
 
 When you're decided on the model (+ config file), there are two ways to train,
@@ -98,8 +108,8 @@ evaluate and infer it:
 Before making choice of an interface, install model's package requirements
 (CLI):
 
-```bash 
-    python -m deeppavlov install <config_path>
+```bash
+python -m deeppavlov install <config_path>
 ```
 
 * where `<config_path>` is path to the chosen model's config file (e.g.
@@ -111,8 +121,8 @@ Before making choice of an interface, install model's package requirements
 
 To get predictions from a model interactively through CLI, run
 
-```bash 
-    python -m deeppavlov interact <config_path> [-d]
+```bash
+python -m deeppavlov interact <config_path> [-d]
 ```
 
 * `-d` downloads required data -- pretrained model files and embeddings
@@ -121,7 +131,7 @@ To get predictions from a model interactively through CLI, run
 You can train it in the same simple way:
 
 ```bash
-    python -m deeppavlov train <config_path> [-d]
+python -m deeppavlov train <config_path> [-d]
 ```
 
 Dataset will be downloaded regardless of whether there was `-d` flag or not.
@@ -132,8 +142,8 @@ The data format is specified in the corresponding model doc page.
 
 There are even more actions you can perform with configs:
 
-```bash 
-    python -m deeppavlov <action> <config_path> [-d]
+```bash
+python -m deeppavlov <action> <config_path> [-d]
 ```
 
 * `<action>` can be
@@ -157,13 +167,13 @@ There are even more actions you can perform with configs:
 
 To get predictions from a model interactively through Python, run
 
-```python 
-    from deeppavlov import build_model
+```python
+from deeppavlov import build_model
 
-    model = build_model(<config_path>, download=True)
+model = build_model(<config_path>, download=True)
 
-    # get predictions for 'input_text1', 'input_text2'
-    model(['input_text1', 'input_text2'])
+# get predictions for 'input_text1', 'input_text2'
+model(['input_text1', 'input_text2'])
 ```
 
 * where `download=True` downloads required data from web -- pretrained model
@@ -175,10 +185,10 @@ To get predictions from a model interactively through Python, run
 
 You can train it in the same simple way:
 
-```python 
-    from deeppavlov import train_model 
+```python
+from deeppavlov import train_model 
 
-    model = train_model(<config_path>, download=True)
+model = train_model(<config_path>, download=True)
 ```
 
 * `download=True` downloads pretrained model, therefore the pretrained
@@ -194,9 +204,9 @@ The data format is specified in the corresponding model doc page.
 You can also calculate metrics on the dataset specified in your config file:
 
 ```python
-    from deeppavlov import evaluate_model 
+from deeppavlov import evaluate_model 
 
-    model = evaluate_model(<config_path>, download=True)
+model = evaluate_model(<config_path>, download=True)
 ```
 
 There are also available integrations with various messengers, see
@@ -206,6 +216,18 @@ and others in the Integrations section for more info.
 
 ## Breaking Changes
 
+**Breaking changes in version 0.6.0**
+- [REST API](http://docs.deeppavlov.ai/en/0.6.0/integrations/rest_api.html):
+  - all models default endpoints were renamed to `/model`
+  - by default model arguments names are taken from `chainer.in`
+    [configuration parameter](http://docs.deeppavlov.ai/en/0.6.0/intro/configuration.html) instead of pre-set names
+    from a [settings file](http://docs.deeppavlov.ai/en/0.6.0/integrations/settings.html)
+  - swagger api endpoint moved from `/apidocs` to `/docs`
+- when using `"max_proba": true` in
+  a [`proba2labels` component](http://docs.deeppavlov.ai/en/0.6.0/apiref/models/classifiers.html) for classification,
+  it will return single label for every batch element instead of a list. One can set `"top_n": 1`
+  to get batches of single item lists as before
+
 **Breaking changes in version 0.5.0**
 - dependencies have to be reinstalled for most pipeline configurations
 - models depending on `tensorflow` require `CUDA 10.0` to run on GPU instead of `CUDA 9.0`
@@ -246,7 +268,7 @@ DeepPavlov is Apache 2.0 - licensed.
 
 ## The Team
 
-DeepPavlov is built and maintained by [Neural Networks and Deep Learning Lab](https://mipt.ru/english/research/labs/neural-networks-and-deep-learning-lab) at [MIPT](https://mipt.ru/english/) within [iPavlov](http://ipavlov.ai/) project (part of [National Technology Initiative](https://asi.ru/eng/nti/)) and in partnership with [Sberbank](http://www.sberbank.com/).
+DeepPavlov is built and maintained by [Neural Networks and Deep Learning Lab](https://mipt.ru/english/research/labs/neural-networks-and-deep-learning-lab) at [MIPT](https://mipt.ru/english/) within [iPavlov](http://ipavlov.ai/) project.
 
 <p align="center">
 <img src="docs/_static/ipavlov_footer.png" width="50%" height="50%"/>

deeppavlov/__init__.py

@@ -37,7 +37,7 @@ def evaluate_model(config: [str, Path, dict], download: bool = False, recursive:
 except ImportError:
     'Assuming that requirements are not yet installed'
 
-__version__ = '0.5.1'
+__version__ = '0.6.0'
 __author__ = 'Neural Networks and Deep Learning lab, MIPT'
 __description__ = 'An open source library for building end-to-end dialog systems and training chatbots.'
 __keywords__ = ['NLP', 'NER', 'SQUAD', 'Intents', 'Chatbot']

deeppavlov/configs/classifiers/insults_kaggle.json

@@ -112,7 +112,7 @@
     "epochs": 1000,
     "batch_size": 64,
     "metrics": [
-      "sets_accuracy",
+      "accuracy",
       "f1_macro",
       {
         "name": "roc_auc",

deeppavlov/configs/classifiers/insults_kaggle_bert.json

@@ -97,7 +97,7 @@
           "y_pred_probas"
         ]
       },
-      "sets_accuracy",
+      "accuracy",
       "f1_macro"
     ],
     "validation_patience": 5,

deeppavlov/configs/classifiers/insults_kaggle_conv_bert.json

@@ -113,7 +113,7 @@
           "y_pred_probas"
         ]
       },
-      "sets_accuracy",
+      "accuracy",
       "f1_macro"
     ],
     "validation_patience": 5,

deeppavlov/configs/classifiers/intents_sample_csv.json

@@ -118,7 +118,7 @@
     "epochs": 100,
     "batch_size": 64,
     "metrics": [
-      "sets_accuracy",
+      "accuracy",
       "f1_macro",
       {
         "name": "roc_auc",

deeppavlov/configs/classifiers/intents_sample_json.json

@@ -113,7 +113,7 @@
     "epochs": 100,
     "batch_size": 64,
     "metrics": [
-      "sets_accuracy",
+      "accuracy",
       "f1_macro",
       {
         "name": "roc_auc",

deeppavlov/configs/classifiers/intents_snips.json

@@ -103,7 +103,7 @@
     "epochs": 1000,
     "batch_size": 64,
     "metrics": [
-      "sets_accuracy",
+      "accuracy",
       "f1_macro",
       {
         "name": "roc_auc",

deeppavlov/configs/classifiers/intents_snips_big.json

@@ -103,7 +103,7 @@
     "epochs": 1000,
     "batch_size": 64,
     "metrics": [
-      "sets_accuracy",
+      "accuracy",
       "f1_macro",
       {
         "name": "roc_auc",

deeppavlov/configs/classifiers/rusentiment_bert.json

@@ -104,7 +104,7 @@
     "metrics": [
       "f1_weighted",
       "f1_macro",
-      "sets_accuracy",
+      "accuracy",
       {
         "name": "roc_auc",
         "inputs": [

deeppavlov/configs/classifiers/rusentiment_bigru_superconv.json

@@ -130,7 +130,7 @@
     "metrics": [
       "f1_weighted",
       "f1_macro",
-      "sets_accuracy",
+      "accuracy",
       {
         "name": "roc_auc",
         "inputs": ["y_onehot", "y_pred_probas"]

deeppavlov/configs/classifiers/rusentiment_cnn.json

@@ -127,7 +127,7 @@
     "batch_size": 64,
     "metrics": [
       "f1_weighted",
-      "sets_accuracy",
+      "accuracy",
       "f1_macro",
       {
         "name": "roc_auc",