🔄 Incremental importance | Acc: 1.000, F1: 1.000

README.md

@@ -6,6 +6,7 @@ tags:
 - sentiment-analysis
 - finbert
 - trading
+
 pipeline_tag: text-classification
 ---
 
@@ -20,17 +21,19 @@ Fine-tuned FinBERT model for financial importance analysis in TradePulse.
 
 ## Performance
 
-*Last training: 2025-07-24 14:48*  
-*Dataset: `news_20250724.csv` (27 samples)*
+*Last training: 2025-07-24 16:00*  
+*Dataset: `news_20250724.csv` (190 samples)*
 
 | Metric | Value |
 |--------|-------|
-| Loss | 0.3668 |
-| Accuracy | 0.8571 |
-| F1 Score | 0.8508 |
-| F1 Macro | 0.8508 |
-| Precision | 0.8857 |
-| Recall | 0.8571 |
+| Loss | 0.1901 |
+| Accuracy | 1.0000 |
+| F1 Score | 1.0000 |
+
+| F1 Macro | 1.0000 |
+
+| Precision | 1.0000 |
+| Recall | 1.0000 |
 
 ## Training Details
 
@@ -41,10 +44,12 @@ Fine-tuned FinBERT model for financial importance analysis in TradePulse.
 - **Batch Size**: 4
 - **Class Balancing**: None
 
+
 ## Usage
 
 ```python
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
 
 tokenizer = AutoTokenizer.from_pretrained("Bencode92/tradepulse-finbert-importance")
 model = AutoModelForSequenceClassification.from_pretrained("Bencode92/tradepulse-finbert-importance")
@@ -53,10 +58,11 @@ model = AutoModelForSequenceClassification.from_pretrained("Bencode92/tradepulse
 text = "Apple reported strong quarterly earnings beating expectations"
 inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
 outputs = model(**inputs)
+
 predictions = outputs.logits.softmax(dim=-1)
 ```
 
 ## Model Card Authors
 
 - TradePulse ML Team
-- Auto-generated on 2025-07-24 14:48:46
+- Auto-generated on 2025-07-24 16:00:13

{checkpoint-5 → checkpoint-48}/model.safetensors

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