# Model is trained on client-manager conversations and is able to classify:

## 1) Greeting class, where managers say 'Hi'. 
##  Uses GRE tag

## 2) Introduction class, where managers introduce themselves.
## Uses NAM tag

## 3) Organisation class, where managers' employer is mentioned
## Uses ORG tag

## 4) Farewell class, where managers say 'Good Bye'
## Uses FAR tag

Evaluation metrics: 
'precision': 0.8695652173913043, 
'recall': 0.9523809523809523, 
'f1_score': 0.909090909090909

{"widget": [{"text": "\u041c\u0435\u043d\u044f \u0437\u043e\u0432\u0443\u0442 A\u043d\u0433\u0435\u043b\u0438\u043d\u0430 \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u044f \u0434\u0438\u0434\u0436\u0438\u0442\u0430\u043b \u0431\u0438\u0437\u043d\u0435\u0441 \u0437\u0432\u043e\u043d\u0438\u043c \u0432\u0430\u043c \u043f\u043e \u043f\u043e\u0432\u043e\u0434\u0443 \u043f\u0440\u043e\u0434\u043b\u0435\u043d\u0438\u044f \u043b\u0438\u0446\u0435\u043d\u0437\u0438\u0438 \u0430 \u043c\u044b \u0441 \u0441\u0435\u0440\u044b\u043c \u0443 \u0432\u0430\u0441 \u0441\u043a\u043e\u0440\u043e \u0441\u0440\u043e\u043a \u0437\u0430\u043a\u0430\u043d\u0447\u0438\u0432\u0430\u0435\u0442\u0441\u044f. My name is Angela digital business company calling you about your license expiration date"}, {"text": "\u0414\u043e\u0431\u0440\u044b\u0439 \u043c\u0435\u043d\u044f \u041c\u0430\u043a\u0441\u0438\u043c \u0437\u043e\u0432\u0443\u0442 \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u044f \u043a\u0438\u0442\u043e\u0431\u0438\u0437\u043d\u0435\u0441 \u0443\u0434\u043e\u0431\u043d\u043e \u0433\u043e\u0432\u043e\u0440\u0438\u0442\u044c. Greetings my name is Maxim I work for Whale Hunter have a minute? "}, {"text": "\u0423\u0433\u0443 \u0434\u0430 \u0432\u0438\u0436\u0443 \u044f \u044d\u0442\u0443 \u043f\u043e\u0447\u0442\u0443 \u0445\u043e\u0440\u043e\u0448\u043e \u0442\u043e\u0433\u0434\u0430 \u0438\u0441\u043f\u0440\u0430\u0432\u043b\u044e \u043d\u0430 \u044d\u0442\u0443 \u0431\u0443\u0434\u0435\u043c \u0436\u0434\u0430\u0442\u044c \u043e\u0442\u0432\u0435\u0442\u0430 \u0432\u0441\u0435\u0433\u043e \u0445\u043e\u0440\u043e\u0448\u0435\u0433\u043e. Ok I see this email sure I will change it shall wait for response good bye"}]}
transformers pytorch bert token-classification autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us 

# Model is trained on client-manager conversations and is able to classify:

## 1) Greeting class, where managers say 'Hi'. 
##  Uses GRE tag

## 2) Introduction class, where managers introduce themselves.
## Uses NAM tag

## 3) Organisation class, where managers' employer is mentioned
## Uses ORG tag

## 4) Farewell class, where managers say 'Good Bye'
## Uses FAR tag

Evaluation metrics: 
'precision': 0.8695652173913043, 
'recall': 0.9523809523809523, 
'f1_score': 0.909090909090909

# Model is trained on client-manager conversations and is able to classify: ## 1) Greeting class, where managers say 'Hi'. ##  Uses GRE tag ## 2) Introduction class, where managers introduce themselves. ## Uses NAM tag ## 3) Organisation class, where managers' employer is mentioned ## Uses ORG tag ## 4) Farewell class, where managers say 'Good Bye' ## Uses FAR tag

Evaluation metrics: 
'precision': 0.8695652173913043, 
'recall': 0.9523809523809523, 
'f1_score': 0.909090909090909
TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us 
 # Model is trained on client-manager conversations and is able to classify: ## 1) Greeting class, where managers say 'Hi'. ##  Uses GRE tag ## 2) Introduction class, where managers introduce themselves. ## Uses NAM tag ## 3) Organisation class, where managers' employer is mentioned ## Uses ORG tag ## 4) Farewell class, where managers say 'Good Bye' ## Uses FAR tag

Evaluation metrics: 
'precision': 0.8695652173913043, 
'recall': 0.9523809523809523, 
'f1_score': 0.909090909090909
passage: TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us 
# Model is trained on client-manager conversations and is able to classify:## 1) Greeting class, where managers say 'Hi'.##  Uses GRE tag## 2) Introduction class, where managers introduce themselves.## Uses NAM tag## 3) Organisation class, where managers' employer is mentioned## Uses ORG tag## 4) Farewell class, where managers say 'Good Bye'## Uses FAR tag

Evaluation metrics: 
'precision': 0.8695652173913043, 
'recall': 0.9523809523809523, 
'f1_score': 0.909090909090909