Update README.md

README.md

@@ -23,9 +23,8 @@ This model performs sentiment analysis with a specific focus on detecting sarcas
 
 This model leverages the **Flan-T5-small** transformer architecture, fine-tuned on datasets including **IMDB** for sentiment analysis and **bharatiyabytes/sentimentWithSarcasm** for sarcasm detection. By combining these datasets, the model is better equipped to differentiate between sarcastic and genuine sentiment expressions, improving sentiment analysis accuracy in contexts where sarcasm is prevalent.
 
-- **Developed by:** [Your Name/Organization]
-- **Funded by [optional]:** [Your Funder, if applicable]
-- **Shared by [optional]:** [Your Organization, if applicable]
+- **Developed by:** bharatiyabytes
+- **Funded by [optional]:** Not yet funded
 - **Model type:** Text Classification
 - **Language(s) (NLP):** English (en)
 - **License:** Apache 2.0
@@ -33,7 +32,7 @@ This model leverages the **Flan-T5-small** transformer architecture, fine-tuned
 
 ### Model Sources [optional]
 
-- **Repository:** [Repository link on Hugging Face]
+- **Repository:** https://github.com/sohi-g/lets-talk-the-hype.git
 - **Paper [optional]:** [Link to paper if available]
 - **Demo [optional]:** [Link to demo if available]
 
@@ -43,7 +42,7 @@ This model leverages the **Flan-T5-small** transformer architecture, fine-tuned
 
 This model can be used directly for sentiment classification on texts where sarcasm may obscure intended sentiment. It provides accurate classifications by considering nuanced expressions, making it ideal for social media analysis, customer feedback processing, and sarcasm-rich content sources.
 
-### Downstream Use [optional]
+### Downstream Use 
 
 This model can serve as a base for further fine-tuning for domains that require sarcasm-aware sentiment analysis, such as customer service, public relations, and social media monitoring applications.
 
@@ -73,7 +72,7 @@ model = AutoModelForSequenceClassification.from_pretrained("your-username/sarcas
 inputs = tokenizer("Your input text here", return_tensors="pt")
 outputs = model(**inputs)
 predictions = torch.argmax(outputs.logits, dim=-1)
-
+```
 ## Training Details
 
 ### Training Data
@@ -82,7 +81,7 @@ The model was fine-tuned on a combination of IMDB (a general sentiment analysis
 
 ### Training Procedure
 
-#### Preprocessing [optional]
+#### Preprocessing
 
 Standard text preprocessing methods were applied, such as tokenization and lowercase transformation.
 
@@ -94,7 +93,7 @@ Epochs: 3
 Batch Size: 16
 Learning Rate: 3e-5
 
-#### Speeds, Sizes, Times [optional]
+#### Speeds, Sizes, Times
 
 Training took approximately 4 hours on an NVIDIA V100 GPU with a model size of 60M parameters.
 
@@ -125,7 +124,7 @@ The model achieved an accuracy of 88% on the sentiment classification task and a
 
 The model shows strong performance in sarcasm-sensitive sentiment analysis, making it suitable for applications where nuanced sentiment interpretation is crucial.
 
-## Model Examination [optional]
+## Model Examination
 
 The model’s predictions have been examined to ensure that sarcastic content is accurately labeled, using interpretability tools such as SHAP to visualize model attention on sarcastic phrases.
 
@@ -141,7 +140,7 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 - **Compute Region:** USA
 - **Carbon Emitted:** NA
 
-## Technical Specifications [optional]
+## Technical Specifications
 
 ### Model Architecture and Objective