Create README.md

README.md

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+SentimentBERT
+Overview
+
+SentimentBERT is a BERT-based model fine-tuned for sentiment analysis on text data. It can classify text into three categories: NEGATIVE, NEUTRAL, and POSITIVE. The model has been trained on a diverse dataset of social media posts, product reviews, and news articles.
+Model Architecture
+
+This model is based on the BERT (Bidirectional Encoder Representations from Transformers) architecture. It consists of 12 transformer layers, 12 attention heads, and a hidden size of 768. The model has been fine-tuned on a large dataset of labeled sentiment data spanning multiple domains and languages.
+Intended Use
+
+This model is designed to analyze text and determine the sentiment expressed within it. It can be used for:
+
+    Social media monitoring and brand reputation management
+    Customer feedback analysis and sentiment tracking
+    Market research and consumer opinion analysis
+    Content moderation and filtering
+    Financial market sentiment analysis
+
+Limitations
+
+    The model may not perform well on domain-specific jargon or slang not present in the training data
+    It may struggle with detecting sarcasm, irony, and nuanced emotional expressions
+    Performance may vary across different languages and cultural contexts
+    The model's performance may degrade on very short texts (less than 3 words) or extremely long texts
+    The model may have biases present in the training data
+
+Example Code
+
+from transformers import AutoTokenizer, AutoModelForSequenceClassificationimport torch# Load model and tokenizertokenizer = AutoTokenizer.from_pretrained("username/sentimentbert")model = AutoModelForSequenceClassification.from_pretrained("username/sentimentbert")# Function to predict sentimentdef predict_sentiment(text):    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)    with torch.no_grad():        outputs = model(**inputs)        predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)    predicted_class = torch.argmax(predictions, dim=1).item()    confidence = predictions[0][predicted_class].item()        sentiment_map = {0: "NEGATIVE", 1: "NEUTRAL", 2: "POSITIVE"}    return sentiment_map[predicted_class], confidence# Example usagetext = "I really enjoyed the movie, the acting was superb!"sentiment, confidence = predict_sentiment(text)print(f"Text: {text}")print(f"Predicted sentiment: {sentiment} (confidence: {confidence:.2f})")
+
+ 
+Training Data 
+
+The model was trained on a combination of publicly available sentiment analysis datasets including: 
+
+     SST-2 (Stanford Sentiment Treebank)
+     IMDB movie reviews
+     Amazon product reviews
+     Twitter sentiment datasets
+     Customer feedback datasets from various industries
+     
+
+Evaluation Results 
+
+The model achieves the following performance metrics on a held-out test set: 
+
+     Accuracy: 92.3%
+     F1 Score (macro): 0.91
+     Precision (macro): 0.92
+     Recall (macro): 0.91
+