sample_model_report.md

Model health report

Index

• Overview
• Config
• Intents
• Entities
• NLU
• Core
• E2E Coverage

Overview

Bot info

Bot Name	Creation date	Updated date
My project	23/10/23 16:22:35	23/10/23 16:22:37

Score

Intent	Entity	NLU	Core	E2E Coverage	Overall
10	-	10	10	10	10
🟢	❌	🟢	🟢	🟢	🟢

Element count

Describe the number of elements in the chatbot.

Element type	Total
Intents	7
Entities	0
Actions and Utters	6
Stories	3
Rules	2

Configs

Settings that were used in the training pipeline and policies.

# The config recipe.
# https://rasa.com/docs/rasa/model-configuration/
recipe: default.v1

# The assistant project unique identifier
# This default value must be replaced with a unique assistant name within your deployment
assistant_id: placeholder_default

# Configuration for Rasa NLU.
# https://rasa.com/docs/rasa/nlu/components/
language: en

pipeline:
# # No configuration for the NLU pipeline was provided. The following default pipeline was used to train your model.
# # If you'd like to customize it, uncomment and adjust the pipeline.
# # See https://rasa.com/docs/rasa/tuning-your-model for more information.
#   - name: WhitespaceTokenizer
#   - name: RegexFeaturizer
#   - name: LexicalSyntacticFeaturizer
#   - name: CountVectorsFeaturizer
#   - name: CountVectorsFeaturizer
#     analyzer: char_wb
#     min_ngram: 1
#     max_ngram: 4
#   - name: DIETClassifier
#     epochs: 100
#     constrain_similarities: true
#   - name: EntitySynonymMapper
#   - name: ResponseSelector
#     epochs: 100
#     constrain_similarities: true
#   - name: FallbackClassifier
#     threshold: 0.3
#     ambiguity_threshold: 0.1

# Configuration for Rasa Core.
# https://rasa.com/docs/rasa/core/policies/
policies:
# # No configuration for policies was provided. The following default policies were used to train your model.
# # If you'd like to customize them, uncomment and adjust the policies.
# # See https://rasa.com/docs/rasa/policies for more information.
#   - name: MemoizationPolicy
#   - name: RulePolicy
#   - name: UnexpecTEDIntentPolicy
#     max_history: 5
#     epochs: 100
#   - name: TEDPolicy
#     max_history: 5
#     epochs: 100
#     constrain_similarities: true

Intents

Section that discusses metrics on model intents.

Metrics

Table with the metrics of intentions.

#		intent	Precision	Recall	F1 Score	Examples
1	🟢	greet	100.0%	100.0%	100.0%	13
2	🟢	affirm	100.0%	100.0%	100.0%	6
3	🟢	deny	100.0%	100.0%	100.0%	7
4	🟢	goodbye	100.0%	100.0%	100.0%	10
5	🟢	mood_great	100.0%	100.0%	100.0%	14
6	🟢	bot_challenge	100.0%	100.0%	100.0%	4
7	🟢	mood_unhappy	100.0%	100.0%	100.0%	14

Confused intentions

Where all the confusing or wrong sentences of the model are listed.

No confusions or errors of intent were found in this model.

Histogram

Confusion Matrix

Entities

Section that discusses metrics about the model entities.

Metrics

Table with entity metrics.

No entities were found in this model.

Confused entities

Where all the confusing or wrong entities of the model are listed.

No confusions of entities were found in this model.

NLU

Section that discusses metrics about NLU and its example phrases.

Sentences

Table with metrics for bot training phrases.

#		Text	Intent	Predicted intent	Confidence	Understood
1	🟢	bye	goodbye	goodbye	100.0%	✅
2	🟢	y	affirm	affirm	100.0%	✅
3	🟢	perfect	mood_great	mood_great	100.0%	✅
4	🟢	great	mood_great	mood_great	100.0%	✅
5	🟢	amazing	mood_great	mood_great	100.0%	✅
6	🟢	feeling like a king	mood_great	mood_great	100.0%	✅
7	🟢	wonderful	mood_great	mood_great	100.0%	✅
8	🟢	I am great	mood_great	mood_great	100.0%	✅
9	🟢	I am amazing	mood_great	mood_great	100.0%	✅
10	🟢	so so perfect	mood_great	mood_great	100.0%	✅
11	🟢	so perfect	mood_great	mood_great	100.0%	✅
12	🟢	I am sad	mood_unhappy	mood_unhappy	100.0%	✅
13	🟢	super sad	mood_unhappy	mood_unhappy	100.0%	✅
14	🟢	sad	mood_unhappy	mood_unhappy	100.0%	✅
15	🟢	very sad	mood_unhappy	mood_unhappy	100.0%	✅
16	🟢	unhappy	mood_unhappy	mood_unhappy	100.0%	✅
17	🟢	extremly sad	mood_unhappy	mood_unhappy	100.0%	✅
18	🟢	so saad	mood_unhappy	mood_unhappy	100.0%	✅
19	🟢	so sad	mood_unhappy	mood_unhappy	100.0%	✅
20	🟢	hey	greet	greet	100.0%	✅
21	🟢	cu	goodbye	goodbye	100.0%	✅
22	🟢	goodbye	goodbye	goodbye	100.0%	✅
23	🟢	yes	affirm	affirm	100.0%	✅
24	🟢	super stoked	mood_great	mood_great	100.0%	✅
25	🟢	extremely good	mood_great	mood_great	100.0%	✅
26	🟢	my day was horrible	mood_unhappy	mood_unhappy	100.0%	✅
27	🟢	I am disappointed	mood_unhappy	mood_unhappy	100.0%	✅
28	🟢	I'm so sad	mood_unhappy	mood_unhappy	100.0%	✅
29	🟢	bye bye	goodbye	goodbye	100.0%	✅
30	🟢	see you later	goodbye	goodbye	100.0%	✅
31	🟢	of course	affirm	affirm	100.0%	✅
32	🟢	I am feeling very good	mood_great	mood_great	100.0%	✅
33	🟢	so good	mood_great	mood_great	100.0%	✅
34	🟢	hi	greet	greet	100.0%	✅
35	🟢	good by	goodbye	goodbye	100.0%	✅
36	🟢	cee you later	goodbye	goodbye	100.0%	✅
37	🟢	I am going to save the world	mood_great	mood_great	100.0%	✅
38	🟢	not very good	mood_unhappy	mood_unhappy	100.0%	✅
39	🟢	hello	greet	greet	100.0%	✅
40	🟢	correct	affirm	affirm	100.0%	✅
41	🟢	hey there	greet	greet	100.0%	✅
42	🟢	hey dude	greet	greet	100.0%	✅
43	🟢	hello there	greet	greet	100.0%	✅
44	🟢	goodmorning	greet	greet	100.0%	✅
45	🟢	goodevening	greet	greet	100.0%	✅
46	🟢	good morning	greet	greet	100.0%	✅
47	🟢	moin	greet	greet	100.0%	✅
48	🟢	good evening	greet	greet	100.0%	✅
49	🟢	I don't feel very well	mood_unhappy	mood_unhappy	100.0%	✅
50	🟢	not good	mood_unhappy	mood_unhappy	100.0%	✅
51	🟢	good afternoon	greet	greet	100.0%	✅
52	🟢	see you around	goodbye	goodbye	100.0%	✅
53	🟢	good night	goodbye	goodbye	100.0%	✅
54	🟢	let's go	greet	greet	100.0%	✅
55	🟢	n	deny	deny	100.0%	✅
56	🟢	no	deny	deny	100.0%	✅
57	🟢	indeed	affirm	affirm	100.0%	✅
58	🟢	never	deny	deny	100.0%	✅
59	🟢	have a nice day	goodbye	goodbye	100.0%	✅
60	🟢	that sounds good	affirm	affirm	100.0%	✅
61	🟢	no way	deny	deny	100.0%	✅
62	🟢	not really	deny	deny	100.0%	✅
63	🟢	I don't think so	deny	deny	100.0%	✅
64	🟢	don't like that	deny	deny	100.0%	✅
65	🟢	am I talking to a human?	bot_challenge	bot_challenge	100.0%	✅
66	🟢	are you a human?	bot_challenge	bot_challenge	100.0%	✅
67	🟢	are you a bot?	bot_challenge	bot_challenge	100.0%	✅
68	🟢	am I talking to a bot?	bot_challenge	bot_challenge	100.0%	✅

Sentences with problems

Table with the sentences that were not understood correctly by the model.

There are no sentences that were not understood in this model.

Core

Section that discusses metrics about bot responses and actions.

Metrics

Table with bot core metrics.

#		Response	Precision	Recall	F1 Score	Number of occurrences
1	🟢	action_listen	100.0%	100.0%	100.0%	16
2	🟢	utter_did_that_help	100.0%	100.0%	100.0%	3
3	🟢	utter_goodbye	100.0%	100.0%	100.0%	4
4	🟢	utter_iamabot	100.0%	100.0%	100.0%	1
5	🟢	utter_greet	100.0%	100.0%	100.0%	5
6	🟢	utter_happy	100.0%	100.0%	100.0%	3
7	🟢	utter_cheer_up	100.0%	100.0%	100.0%	3

Confusion Matrix

E2E Coverage

Section that shows data from intents and responses that aren't covered by end-to-end tests.

Not covered elements

List with not covered elements by end-to-end tests.

Intents

• (no elements not covered)

Actions

• (no elements not covered)

Total number of elements: 13

Total number of not covered elements: 0

Total number of excluded elements: 0

Coverage rate: 100.0% (🟢)

Generated by rasa-model-report v1.4.2b14, collaborative open-source project for Rasa projects. Github repository at this link.