Upload README.md with huggingface_hub

README.md

@@ -1,3 +1,4 @@
+
 ---
 language: en
 license: apache-2.0
@@ -6,18 +7,17 @@ tags:
 - sentiment-analysis
 - modernbert
 - financial-nlp
-- unsloth
 datasets:
 - neoyipeng/financial_reasoning_aggregated
 metrics:
 - accuracy
 widget:
-- text: The company reported strong quarterly earnings with revenue beating expectations.
-  example_title: Positive Example
-- text: Stock prices fell sharply following disappointing guidance from management.
-  example_title: Negative Example
-- text: The merger is expected to close in Q3 pending regulatory approval.
-  example_title: Neutral Example
+- text: "The company reported strong quarterly earnings with revenue beating expectations."
+  example_title: "Positive Example"
+- text: "Stock prices fell sharply following disappointing guidance from management."
+  example_title: "Negative Example"
+- text: "The merger is expected to close in Q3 pending regulatory approval."
+  example_title: "Neutral Example"
 ---
 
 # ModernFinBERT: Financial Sentiment Analysis
@@ -33,7 +33,7 @@ ModernFinBERT is a financial sentiment analysis model based on the ModernBERT ar
 
 ## Performance
 
-- **FinancialPhraseBank Accuracy**: 90.47%
+- **FinancialPhraseBank Accuracy**: 91.21%
 - **Target**: >94% accuracy on FinancialPhraseBank
 
 ## Usage
@@ -50,7 +50,7 @@ inputs = tokenizer(text, return_tensors="pt")
 outputs = model(**inputs)
 predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
 
-labels = ["NEGATIVE", "NEUTRAL", "POSITIVE"] 
+labels = ["NEGATIVE", "NEUTRAL", "POSITIVE"]
 predicted_class = labels[predictions.argmax().item()]
 confidence = predictions.max().item()