Update README.md

README.md

@@ -2,25 +2,40 @@
 library_name: transformers
 language:
 - en
+widget:
+ - text: "Your task is to classify sentences' sentiment as 'positive' or 'negative'. Your answer should be one word, either 'positive' or 'negative'.
+Sentence: {text}
+Answer: "
 pipeline_tag: text-generation
 tags:
 - nlp
 ---
 
-# Model Card for Model ID
+# Model Card for Phi 1.5B Microsoft Trained Sentiment Analysis Model
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-
+This model performs sentiment analysis on sentences, classifying them as either 'positive' or 'negative'. It is trained on the IMDB dataset and has been fine-tuned for this task.
 
 ## Model Details
 
 ### Model Description
 
-Phi 1.5B Microsoft trained with IMDB Dataset.
+Phi 1.5B Microsoft trained with the IMDB Dataset.
 
 ### Prompt Used in Training
 
-"""Your task is to classify sentences' sentiment as 'positive' or 'negative'. Your answer should be one word, either 'positive' or 'negative'.
+Your task is to classify sentences' sentiment as 'positive' or 'negative'. Your answer should be one word, either 'positive' or 'negative'.
 Sentence: {text}
-Answer: """
+Answer:
+
+
+## Inference Example using Hugging Face Inference API
+
+```python
+from transformers import pipeline
+
+classifier = pipeline("text-classification", model="matheusrdgsf/phi-sentiment-analysis-model")
+
+result = classifier("I love this movie")
+print(result[0]['label'])  # Output: 'POSITIVE'