Create README

README.md

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+# 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")
+```