Update README.md

README.md

@@ -8,6 +8,8 @@ metrics:
 model-index:
 - name: med_nonmed
   results: []
+datasets:
+- ai-maker-space/medical_nonmedical
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -15,22 +17,25 @@ should probably proofread and complete it, then remove this comment. -->
 
 # med_nonmed
 
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the [Medical/Non-Medical Dataset](https://huggingface.co/datasets/ai-maker-space/medical_nonmedical) dataset.
+
 It achieves the following results on the evaluation set:
 - Loss: 0.0135
 - F1: 0.9934
 
 ## Model description
 
-More information needed
+A fine-tuned classifier based on the DistilBERT Base Uncased model. 
 
 ## Intended uses & limitations
 
-More information needed
+This model can be used for rough filtering of medical/non-medical text. Potential use-case includes filtering emails.
+
+The model is in v0 and should not be used in critical functions without proper evaluation and risk assessment. 
 
 ## Training and evaluation data
 
-More information needed
+The model was trained on a custom dataset that can be found [here](https://huggingface.co/datasets/ai-maker-space/medical_nonmedical) which is a composite of two separate medical and non-medical datasets.
 
 ## Training procedure
 
@@ -58,4 +63,4 @@ The following hyperparameters were used during training:
 - Transformers 4.34.0
 - Pytorch 2.0.1+cu118
 - Datasets 2.14.5
-- Tokenizers 0.14.1
+- Tokenizers 0.14.1