# YaleParser  Resumes Classification

**YaleParser** is a python tool for NLP classification Task and generate databases with this classification. This model is a fineting on named-entity-recognition and zero-shot sequence classifiers. The method works by posing the sequence to be classified as the NLI and Bayesian weigths to construct hypothesis from each candidate label, and stepwise with regex, build a Database.


### Design 

```
predict_single('''08/1992-05/1996 BA, Biology, West Virginia University, Morgantown, WV''')

# 'Education'
```







                                     precision    recall  f1-score   support

            Administrative Position      0.73      0.73      0.73        49
            Appointments                 0.73      0.84      0.79       115
            Bibliography                 0.94      0.83      0.88        87
            Board Certification          0.94      0.77      0.85        44
            Education                    0.86      0.86      0.86       100
            Grants/Clinical Trials       0.94      0.85      0.89        40
            Other                        0.69      0.77      0.73       156
            Patents                      0.98      0.98      0.98        43
            Professional Honors          0.80      0.85      0.82       170
            Professional Service         0.85      0.61      0.71        85

               accuracy                           0.81       889
              macro avg       0.85      0.81      0.82       889
           weighted avg       0.82      0.81      0.81       889

{}
region:us
TAGS
#region-us 

# YaleParser  Resumes Classification

YaleParser is a python tool for NLP classification Task and generate databases with this classification. This model is a fineting on named-entity-recognition and zero-shot sequence classifiers. The method works by posing the sequence to be classified as the NLI and Bayesian weigths to construct hypothesis from each candidate label, and stepwise with regex, build a Database.


### Design 









                                     precision    recall  f1-score   support

            Administrative Position      0.73      0.73      0.73        49
            Appointments                 0.73      0.84      0.79       115
            Bibliography                 0.94      0.83      0.88        87
            Board Certification          0.94      0.77      0.85        44
            Education                    0.86      0.86      0.86       100
            Grants/Clinical Trials       0.94      0.85      0.89        40
            Other                        0.69      0.77      0.73       156
            Patents                      0.98      0.98      0.98        43
            Professional Honors          0.80      0.85      0.82       170
            Professional Service         0.85      0.61      0.71        85

               accuracy                           0.81       889
              macro avg       0.85      0.81      0.82       889
           weighted avg       0.82      0.81      0.81       889

# YaleParser  Resumes Classification

YaleParser is a python tool for NLP classification Task and generate databases with this classification. This model is a fineting on named-entity-recognition and zero-shot sequence classifiers. The method works by posing the sequence to be classified as the NLI and Bayesian weigths to construct hypothesis from each candidate label, and stepwise with regex, build a Database. ### Design 









                                     precision    recall  f1-score   support

            Administrative Position      0.73      0.73      0.73        49
            Appointments                 0.73      0.84      0.79       115
            Bibliography                 0.94      0.83      0.88        87
            Board Certification          0.94      0.77      0.85        44
            Education                    0.86      0.86      0.86       100
            Grants/Clinical Trials       0.94      0.85      0.89        40
            Other                        0.69      0.77      0.73       156
            Patents                      0.98      0.98      0.98        43
            Professional Honors          0.80      0.85      0.82       170
            Professional Service         0.85      0.61      0.71        85

               accuracy                           0.81       889
              macro avg       0.85      0.81      0.82       889
           weighted avg       0.82      0.81      0.81       889
TAGS
#region-us 
 # YaleParser  Resumes Classification

YaleParser is a python tool for NLP classification Task and generate databases with this classification. This model is a fineting on named-entity-recognition and zero-shot sequence classifiers. The method works by posing the sequence to be classified as the NLI and Bayesian weigths to construct hypothesis from each candidate label, and stepwise with regex, build a Database. ### Design 









                                     precision    recall  f1-score   support

            Administrative Position      0.73      0.73      0.73        49
            Appointments                 0.73      0.84      0.79       115
            Bibliography                 0.94      0.83      0.88        87
            Board Certification          0.94      0.77      0.85        44
            Education                    0.86      0.86      0.86       100
            Grants/Clinical Trials       0.94      0.85      0.89        40
            Other                        0.69      0.77      0.73       156
            Patents                      0.98      0.98      0.98        43
            Professional Honors          0.80      0.85      0.82       170
            Professional Service         0.85      0.61      0.71        85

               accuracy                           0.81       889
              macro avg       0.85      0.81      0.82       889
           weighted avg       0.82      0.81      0.81       889
passage: TAGS
#region-us 
# YaleParser  Resumes Classification

YaleParser is a python tool for NLP classification Task and generate databases with this classification. This model is a fineting on named-entity-recognition and zero-shot sequence classifiers. The method works by posing the sequence to be classified as the NLI and Bayesian weigths to construct hypothesis from each candidate label, and stepwise with regex, build a Database.### Design 









                                     precision    recall  f1-score   support

            Administrative Position      0.73      0.73      0.73        49
            Appointments                 0.73      0.84      0.79       115
            Bibliography                 0.94      0.83      0.88        87
            Board Certification          0.94      0.77      0.85        44
            Education                    0.86      0.86      0.86       100
            Grants/Clinical Trials       0.94      0.85      0.89        40
            Other                        0.69      0.77      0.73       156
            Patents                      0.98      0.98      0.98        43
            Professional Honors          0.80      0.85      0.82       170
            Professional Service         0.85      0.61      0.71        85

               accuracy                           0.81       889
              macro avg       0.85      0.81      0.82       889
           weighted avg       0.82      0.81      0.81       889