# HCAHPS survey comments multilabel classification

This model is a fine-tuned version of [Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on a dataset of HCAHPS survey comments.

It achieves the following results on the evaluation set:
                     
                 precision    recall  f1-score   support

    medical          0.87      0.81      0.84        83
    environmental    0.77      0.91      0.84        93
    administration   0.58      0.32      0.41        22
    communication    0.85      0.82      0.84        50
    condition        0.42      0.52      0.46        29
    treatment        0.90      0.78      0.83        68
    food             0.92      0.94      0.93        36
    clean            0.65      0.83      0.73        18
    bathroom         0.64      0.64      0.64        14
    discharge        0.83      0.83      0.83        24
    wait             0.96      1.00      0.98        24
    financial        0.44      1.00      0.62         4
    extra_nice       0.20      0.13      0.16        23
    rude             1.00      0.64      0.78        11
    nurse            0.92      0.98      0.95       110
    doctor           0.96      0.84      0.90        57

    micro avg        0.81      0.81      0.81       666
    macro avg        0.75      0.75      0.73       666
    weighted avg     0.82      0.81      0.81       666
    samples avg      0.64      0.64      0.62       666

## Model description

The model classifies free-text comments into the following labels

* Medical
* Environmental
* Administration
* Communication
* Condition
* Treatment
* Food
* Clean
* Bathroom
* Discharge
* Wait
* Financial
* Extra_nice
* Rude
* Nurse
* Doctor 

## How to use

You can now use the models directly through the transformers library. Check out the [model's page](https://huggingface.co/joniponi/multilabel_inpatient_comments_16labels) for instructions on how to use the models within the Transformers library.

Load the model via the transformers library:
```
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("joniponi/multilabel_inpatient_comments_16labels")
model = AutoModel.from_pretrained("joniponi/multilabel_inpatient_comments_16labels")
```

{}
transformers pytorch tensorboard bert text-classification autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us 

# HCAHPS survey comments multilabel classification

This model is a fine-tuned version of Bio_ClinicalBERT on a dataset of HCAHPS survey comments.

It achieves the following results on the evaluation set:
                     
                 precision    recall  f1-score   support

    medical          0.87      0.81      0.84        83
    environmental    0.77      0.91      0.84        93
    administration   0.58      0.32      0.41        22
    communication    0.85      0.82      0.84        50
    condition        0.42      0.52      0.46        29
    treatment        0.90      0.78      0.83        68
    food             0.92      0.94      0.93        36
    clean            0.65      0.83      0.73        18
    bathroom         0.64      0.64      0.64        14
    discharge        0.83      0.83      0.83        24
    wait             0.96      1.00      0.98        24
    financial        0.44      1.00      0.62         4
    extra_nice       0.20      0.13      0.16        23
    rude             1.00      0.64      0.78        11
    nurse            0.92      0.98      0.95       110
    doctor           0.96      0.84      0.90        57

    micro avg        0.81      0.81      0.81       666
    macro avg        0.75      0.75      0.73       666
    weighted avg     0.82      0.81      0.81       666
    samples avg      0.64      0.64      0.62       666

## Model description

The model classifies free-text comments into the following labels

* Medical
* Environmental
* Administration
* Communication
* Condition
* Treatment
* Food
* Clean
* Bathroom
* Discharge
* Wait
* Financial
* Extra_nice
* Rude
* Nurse
* Doctor 

## How to use

You can now use the models directly through the transformers library. Check out the model's page for instructions on how to use the models within the Transformers library.

Load the model via the transformers library:


# HCAHPS survey comments multilabel classification

This model is a fine-tuned version of Bio_ClinicalBERT on a dataset of HCAHPS survey comments.

It achieves the following results on the evaluation set:
                     
                 precision    recall  f1-score   support

    medical          0.87      0.81      0.84        83
    environmental    0.77      0.91      0.84        93
    administration   0.58      0.32      0.41        22
    communication    0.85      0.82      0.84        50
    condition        0.42      0.52      0.46        29
    treatment        0.90      0.78      0.83        68
    food             0.92      0.94      0.93        36
    clean            0.65      0.83      0.73        18
    bathroom         0.64      0.64      0.64        14
    discharge        0.83      0.83      0.83        24
    wait             0.96      1.00      0.98        24
    financial        0.44      1.00      0.62         4
    extra_nice       0.20      0.13      0.16        23
    rude             1.00      0.64      0.78        11
    nurse            0.92      0.98      0.95       110
    doctor           0.96      0.84      0.90        57

    micro avg        0.81      0.81      0.81       666
    macro avg        0.75      0.75      0.73       666
    weighted avg     0.82      0.81      0.81       666
    samples avg      0.64      0.64      0.62       666 ## Model description

The model classifies free-text comments into the following labels

* Medical
* Environmental
* Administration
* Communication
* Condition
* Treatment
* Food
* Clean
* Bathroom
* Discharge
* Wait
* Financial
* Extra_nice
* Rude
* Nurse
* Doctor ## How to use

You can now use the models directly through the transformers library. Check out the model's page for instructions on how to use the models within the Transformers library.

Load the model via the transformers library:
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us 
 # HCAHPS survey comments multilabel classification

This model is a fine-tuned version of Bio_ClinicalBERT on a dataset of HCAHPS survey comments.

It achieves the following results on the evaluation set:
                     
                 precision    recall  f1-score   support

    medical          0.87      0.81      0.84        83
    environmental    0.77      0.91      0.84        93
    administration   0.58      0.32      0.41        22
    communication    0.85      0.82      0.84        50
    condition        0.42      0.52      0.46        29
    treatment        0.90      0.78      0.83        68
    food             0.92      0.94      0.93        36
    clean            0.65      0.83      0.73        18
    bathroom         0.64      0.64      0.64        14
    discharge        0.83      0.83      0.83        24
    wait             0.96      1.00      0.98        24
    financial        0.44      1.00      0.62         4
    extra_nice       0.20      0.13      0.16        23
    rude             1.00      0.64      0.78        11
    nurse            0.92      0.98      0.95       110
    doctor           0.96      0.84      0.90        57

    micro avg        0.81      0.81      0.81       666
    macro avg        0.75      0.75      0.73       666
    weighted avg     0.82      0.81      0.81       666
    samples avg      0.64      0.64      0.62       666 ## Model description

The model classifies free-text comments into the following labels

* Medical
* Environmental
* Administration
* Communication
* Condition
* Treatment
* Food
* Clean
* Bathroom
* Discharge
* Wait
* Financial
* Extra_nice
* Rude
* Nurse
* Doctor ## How to use

You can now use the models directly through the transformers library. Check out the model's page for instructions on how to use the models within the Transformers library.

Load the model via the transformers library:
passage: TAGS
#transformers #pytorch #tensorboard #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us 
# HCAHPS survey comments multilabel classification

This model is a fine-tuned version of Bio_ClinicalBERT on a dataset of HCAHPS survey comments.

It achieves the following results on the evaluation set:
                     
                 precision    recall  f1-score   support

    medical          0.87      0.81      0.84        83
    environmental    0.77      0.91      0.84        93
    administration   0.58      0.32      0.41        22
    communication    0.85      0.82      0.84        50
    condition        0.42      0.52      0.46        29
    treatment        0.90      0.78      0.83        68
    food             0.92      0.94      0.93        36
    clean            0.65      0.83      0.73        18
    bathroom         0.64      0.64      0.64        14
    discharge        0.83      0.83      0.83        24
    wait             0.96      1.00      0.98        24
    financial        0.44      1.00      0.62         4
    extra_nice       0.20      0.13      0.16        23
    rude             1.00      0.64      0.78        11
    nurse            0.92      0.98      0.95       110
    doctor           0.96      0.84      0.90        57

    micro avg        0.81      0.81      0.81       666
    macro avg        0.75      0.75      0.73       666
    weighted avg     0.82      0.81      0.81       666
    samples avg      0.64      0.64      0.62       666## Model description

The model classifies free-text comments into the following labels

* Medical
* Environmental
* Administration
* Communication
* Condition
* Treatment
* Food
* Clean
* Bathroom
* Discharge
* Wait
* Financial
* Extra_nice
* Rude
* Nurse
* Doctor## How to use

You can now use the models directly through the transformers library. Check out the model's page for instructions on how to use the models within the Transformers library.

Load the model via the transformers library: