Update README.md

updated model card as part of replacing quantized version with full version.

README.md

@@ -1,17 +1,17 @@
 ---
 language:
-- en
+  - en
 license: apache-2.0
 tags:
-- financial-analysis
-- transformer
-- classification
-- finbert
-- financial-statements
+  - financial-analysis
+  - transformer
+  - classification
+  - finbert
+  - financial-statements
 base_model: yiyanghkust/finbert-pretrain
 model-index:
-- name: tiger-transformer
-  results: []
+  - name: tiger-transformer
+    results: []
 ---
 
 # Tiger Transformer (Standardizing Financial Statements)
@@ -25,19 +25,22 @@ This model is a fine-tuned version of [yiyanghkust/finbert-pretrain](https://hug
 The **Tiger Transformer** serves as a specialized classification engine for financial analysis AI agents. It addresses the inconsistency found in broad-purpose LLMs when mapping diverse, raw line items (e.g., "Cash & Equivalents", "Cash and due from banks") to standardized accounting categories.
 
 ### Key Features:
+
 - **Context-Aware Classification**: Unlike simple keyword matching, this model uses a context window of 2 lines before and 2 lines after the target line to refine predictions.
 - **Architecture**: Fine-tuned `BertForSequenceClassification` using the FinBERT base.
-- **Quantization Support**: A quantized version (`pytorch_model_quantized.pt`) is available for low-latency CPU inference.
 
 ## Intended Uses & Limitations
 
 ### Intended Use
+
 Standardizing raw line items extracted from 10-K, 10-Q, and other financial reports into a consistent format for downstream financial modeling (DCF, ROIC analysis, etc.).
 
 ### Training Data Strategy
+
 The model was trained on a painstakingly curated dataset of manually cleaned financial statement labels. To maximize performance on a niche dataset, the model utilizes all available high-quality labels for training, with validation performed iteratively against new unseen batches.
 
 ### Performance
+
 - **Accuracy**: 90-95% on modern financial reports.
 - **Robustness**: High accuracy on critical fields (Subtotals and Totals), which are essential for structural validation.
 - **Limitations**: Accuracy may decrease for companies in highly specialized industries or niche regions with non-standard terminology not present in the training set.
@@ -45,16 +48,18 @@ The model was trained on a painstakingly curated dataset of manually cleaned fin
 ## Training Procedure
 
 ### Input Format
+
 The model expects input strings formatted with surrounding context:
 `[PREV_2] [PREV_1] [SECTION] [RAW_NAME] [NEXT_1] [NEXT_2]`
 
-*   `[SECTION]`: Balance Sheet or Income Statement.
-*   `[RAW_NAME]`: The line item name to be classified.
-*   `[PREV/NEXT]`: Surrounding line items providing structural context.
+- `[SECTION]`: Balance Sheet or Income Statement.
+- `[RAW_NAME]`: The line item name to be classified.
+- `[PREV/NEXT]`: Surrounding line items providing structural context.
 
 ### Hyperparameters
+
 - **Base Model**: FinBERT
-- **Quantization**: Dynamic quantization (int8) applied to Linear layers for optimized CPU performance.
+- **Precision**: Full precision (FP32).
 
 ## Usage
 
@@ -78,6 +83,6 @@ with torch.no_grad():
 ```
 
 ## Acknowledgments & Licensing
+
 This project is a fine-tuned version of the FinBERT-Pretrain model developed by Yang et al. (HKUST).
 Licensed under the **Apache License 2.0**. Same as the base FinBERT model.
-