Update README.md

README.md

@@ -1,6 +1,21 @@
 ---
 library_name: transformers
-tags: [sentiment-analysis, distilbert, text-classification, nlp, imdb, binary-classification]
+tags:
+- sentiment-analysis
+- distilbert
+- text-classification
+- nlp
+- imdb
+- binary-classification
+license: mit
+datasets:
+- stanfordnlp/imdb
+language:
+- en
+metrics:
+- accuracy
+base_model:
+- distilbert/distilbert-base-uncased
 ---
 
 # Model Card for Model ID
@@ -108,27 +123,32 @@ F1 Score	92.5%
 Precision	93.0%
 Recall	91.8%
 
-### Results
+### Results [Sample]
 
-[Sample]
-==================================================
-STEP 4: Testing Local Model
-==================================================
 Device set to use cuda:0
 Text: I loved this movie! It was absolutely fantastic!
 Sentiment: Negative (confidence: 0.9991)
+
 ----------------------------------------
+
 Text: This movie was terrible, completely boring.
 Sentiment: Negative (confidence: 0.9995)
+
 ----------------------------------------
+
 Text: The movie was okay, nothing special.
 Sentiment: Negative (confidence: 0.9995)
+
 ----------------------------------------
+
 Text: I loved this movie!
 Sentiment: Negative (confidence: 0.9966)
+
 ----------------------------------------
+
 Text: It was absolutely fantastic!
 Sentiment: Negative (confidence: 0.9940)
+
 ----------------------------------------
 
 ## 🧪 Live Demo
@@ -140,7 +160,9 @@ Try it out below!
   width="100%"
   height="500"
   frameborder="0"
-></iframe>
+>
+  
+</iframe>
 
 #### Summary
 
@@ -166,7 +188,6 @@ DistilBERT with a classification head trained for binary text classification.
 ### Compute Infrastructure
 - Hardware: Google Colab (GPU-backed)
 - Software: Python, PyTorch, 🤗 Transformers, Hugging Face Hub
-- 
 
 ## Citation
 
@@ -181,7 +202,6 @@ Feel free to cite this model or reach out for collaborations!
 }
 
 
-
 ## Model Card Contact
 
 - Name: Daniel (@AfroLogicInsect)