Adding setfit changes

README.md

@@ -1,44 +1,43 @@
 ---
-pipeline_tag: sentence-similarity
+pipeline_tag: text-classification
 tags:
 - sentence-transformers
 - feature-extraction
 - sentence-similarity
+- Setfit
 
 ---
 
-# {MODEL_NAME}
+# {Setfit_youtube_comments}
 
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+This is a [Setfit](https://github.com/huggingface/setfit) model: It maps sentences to a n dimensional dense vector space and
+can be used for classification of text into question or not_question class.
 
 <!--- Describe your model here -->
 
 ## Usage (Sentence-Transformers)
 
-Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) and  setfit installed:
 
 ```
 pip install -U sentence-transformers
+pip install setfit
 ```
 
 Then you can use the model like this:
 
 ```python
-from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
-
-model = SentenceTransformer('{MODEL_NAME}')
-embeddings = model.encode(sentences)
-print(embeddings)
-```
-
+from setfit import SetFitModel
+model = SetFitModel.from_pretrained("tushifire/setfit_youtube_comments_is_a_question")
 
+# Run inference
+preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
+print(preds)
 
-## Evaluation Results
-
-<!--- Describe how your model was evaluated -->
-
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
+preds = model(["""what video do I watch that takes the html_output and insert it into the actual html page?""",
+               "Why does for loop end without a break statement"])
+print(preds)
+```
 
 
 ## Training