Update README.md

README.md

@@ -1,21 +1,31 @@
 ---
-license: apache-2.0
-base_model: distilbert-base-uncased
+language: "en"
 tags:
-- generated_from_trainer
-metrics:
-- accuracy
-model-index:
-- name: distilbert-base-uncased-finetuned-text-classification
-  results: []
+- distilbert-base-uncased
+- text-classification
+- patient
+- doctor 
+
+widget:
+- text: "I've got flu"
+- text: "I prescribe you some drugs and you need to stay at home for a couple of days"
+- text: "Let's move to the theatre this evening!"
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 # distilbert-base-uncased-finetuned-text-classification
 
 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
+
+# Fine-tuned DistilBERT-base-uncased for Patient-Doctor Classification
+
+# Model Description 
+
+DistilBERT is a transformer model that performs text classification. I fine-tuned the model on with the purpose of classifying patient, doctor or neutral content, specifically when text is related to the supposed context. The model predicts 3 classes, which are Patient, Doctor or Neutral. 
+
+The model is a fine-tuned version of [DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert).
+
+It was fine-tuned on the prepared dataset (https://huggingface.co/datasets/LukeGPT88/text-classification-dataset).
+
 It achieves the following results on the evaluation set:
 - Loss: 0.0501
 - Accuracy: 0.9861
@@ -59,3 +69,50 @@ The following hyperparameters were used during training:
 - Pytorch 2.1.2
 - Datasets 2.1.0
 - Tokenizers 0.15.1
+
+---
+language: "en"
+tags:
+- distilroberta
+- sentiment
+- NSFW
+- inappropriate 
+- spam
+- twitter
+- reddit
+
+widget:
+- text: "I like you. You remind me of me when I was young and stupid."
+- text: "I see you’ve set aside this special time to humiliate yourself in public."
+- text: "Have a great weekend! See you next week!"
+
+---
+
+# Fine-tuned DistilRoBERTa-base for NSFW Classification
+
+# Model Description 
+
+DistilBERT is a transformer model that performs sentiment analysis. I fine-tuned the model on Reddit posts with the purpose of classifying not safe for work (NSFW) content, specifically text that is considered inappropriate and unprofessional. The model predicts 2 classes, which are NSFW or safe for work (SFW). 
+
+The model is a fine-tuned version of [DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert).
+
+It was fine-tuned on 14317 Reddit posts pulled from the (Reddit API) [https://praw.readthedocs.io/en/stable/].
+
+# How to Use 
+
+```python
+from transformers import pipeline
+classifier = pipeline("sentiment-analysis", model="michellejieli/NSFW_text_classification")
+classifier("I see you’ve set aside this special time to humiliate yourself in public.")
+```
+
+```python
+Output:
+[{'label': 'NSFW', 'score': 0.998853325843811}]
+```
+
+# Contact
+
+Please reach out to [[email protected]]([email protected]) if you have any questions or feedback.
+
+---