Update README.md

README.md

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-# FinBERT Fine-Tuned for Financial Sentiment Analysis
-
-This repository contains a fine-tuned version of FinBERT (RoBERTa-based) for financial sentiment classification. The model predicts whether a financial news headline or sentence is **positive**, **neutral**, or **negative**.
-
-## Model Overview
-- **Base model:** FinBERT (RoBERTa)
-- **Task:** Financial sentiment classification (3 classes)
-- **Training data:** Financial news headlines and sentences
-- **Dataset source:** [Kaggle - Finance News Sentiments](https://www.kaggle.com/datasets/antobenedetti/finance-news-sentiments/data?select=dataset.csv)
-- **Output labels:**
-  - 0: Negative
-  - 1: Neutral
-  - 2: Positive
-
-## Evaluation Results
-- **Test Accuracy:** 0.7565
-- **Multiclass ROC AUC (macro-average):** 0.9096
-
-## Model Folder Structure
-```
-finbert_finetuned/
-    config.json
-    merges.txt
-    model.safetensors
-    special_tokens_map.json
-    tokenizer_config.json
-    tokenizer.json
-    vocab.json
-```
-**Note:** Only the model files are stored in `finbert_finetuned_news_sentiment/`. Scripts and datasets are kept separate and are not included in this folder or in the model upload.
-
-## How to Use the Fine-Tuned Model
-
-### 1. Load and Use the Model in Python
-```python
-import torch
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-# Directory of the model folder
-model_dir = "finbert_finetuned_news_sentiment"
-# read the model
-tokenizer = AutoTokenizer.from_pretrained(model_dir)
-model = AutoModelForSequenceClassification.from_pretrained(model_dir)
-model.eval()
-
-text = "Apple stock surges after strong earnings report."
-inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
-with torch.no_grad():
-    logits = model(**inputs).logits
-    pred = torch.argmax(logits, dim=1).item()
-
-label_map = {0: 'negative', 1: 'neutral', 2: 'positive'}
-print(f"Predicted sentiment: {label_map[pred]}")
-```
-
-## Notes
-- The model was trained and evaluated on data from the Kaggle dataset linked above.
-- The `finbert_finetuned_news_sentiment/` folder contains only the files needed for inference.
-- Scripts and datasets are not included in the model folder or in the model upload.
-- For best results, use a GPU for inference if available.
-
-## Citation
-If you use this model, please cite the original [FinBERT paper](https://arxiv.org/abs/2006.08097) and the [Kaggle dataset](https://www.kaggle.com/datasets/antobenedetti/finance-news-sentiments/data?select=dataset.csv).
-
----
-
-**Date:** June 2025
+---
+language: en
+license: apache-2.0
+tags:
+- finance
+- sentiment-analysis
+- finbert
+- roberta
+- classification
+model-index:
+- name: FinBERT Fine-Tuned for Financial Sentiment Analysis
+  results:
+  - task:
+      type: text-classification
+      name: Sentiment Analysis
+    dataset:
+      name: Finance News Sentiments (Kaggle)
+      type: text
+    metrics:
+    - type: accuracy
+      value: 0.7565
+    - type: multiclass_roc_auc
+      value: 0.9096
+base_model:
+- FacebookAI/roberta-large
+---
+
+# FinBERT Fine-Tuned for Financial Sentiment Analysis
+
+This repository contains a fine-tuned version of FinBERT (RoBERTa-based) for financial sentiment classification. The model predicts whether a financial news headline or sentence is **positive**, **neutral**, or **negative**.
+
+## Model Overview
+- **Base model:** FinBERT (RoBERTa)
+- **Task:** Financial sentiment classification (3 classes)
+- **Training data:** Financial news headlines and sentences
+- **Dataset source:** [Kaggle - Finance News Sentiments](https://www.kaggle.com/datasets/antobenedetti/finance-news-sentiments/data?select=dataset.csv)
+- **Output labels:**
+  - 0: Negative
+  - 1: Neutral
+  - 2: Positive
+
+## Evaluation Results
+- **Test Accuracy:** 0.7565
+- **Multiclass ROC AUC (macro-average):** 0.9096
+
+## Model Folder Structure
+```
+finbert_finetuned/
+    config.json
+    merges.txt
+    model.safetensors
+    special_tokens_map.json
+    tokenizer_config.json
+    tokenizer.json
+    vocab.json
+```
+**Note:** Only the model files are stored in `finbert_finetuned_news_sentiment/`. Scripts and datasets are kept separate and are not included in this folder or in the model upload.
+
+## How to Use the Fine-Tuned Model
+
+### 1. Load and Use the Model in Python
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+# Directory of the model folder
+model_dir = "finbert_finetuned_news_sentiment"
+# read the model
+tokenizer = AutoTokenizer.from_pretrained(model_dir)
+model = AutoModelForSequenceClassification.from_pretrained(model_dir)
+model.eval()
+
+text = "Apple stock surges after strong earnings report."
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
+with torch.no_grad():
+    logits = model(**inputs).logits
+    pred = torch.argmax(logits, dim=1).item()
+
+label_map = {0: 'negative', 1: 'neutral', 2: 'positive'}
+print(f"Predicted sentiment: {label_map[pred]}")
+```
+
+## Notes
+- The model was trained and evaluated on data from the Kaggle dataset linked above.
+- The `finbert_finetuned_news_sentiment/` folder contains only the files needed for inference.
+- Scripts and datasets are not included in the model folder or in the model upload.
+- For best results, use a GPU for inference if available.
+
+## Citation
+If you use this model, please cite the original [FinBERT paper](https://arxiv.org/abs/2006.08097) and the [Kaggle dataset](https://www.kaggle.com/datasets/antobenedetti/finance-news-sentiments/data?select=dataset.csv).
+
+---
+
+**Date:** June 2025