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README.md

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+# FinBERT-Multilingual-Intent-and-Sentiment
+
+## Overview
+`FinBERT-Multilingual-Intent-and-Sentiment` is a BERT-based model fine-tuned for joint multi-label classification on financial customer service messages. It is designed to simultaneously classify the **Intent** (e.g., 'Account_Access_Issue', 'Mortgage_Inquiry') and the **Sentiment/Urgency** (e.g., 'Negative', 'Anxious', 'Positive') of a message. The model is trained on a multilingual corpus, supporting English (en), Spanish (es), French (fr), German (de), and Portuguese (pt).
+
+The model leverages the robustness of a multilingual BERT base and adapts it specifically for the high-stakes financial domain, making it ideal for automating customer service routing and prioritization.
+
+## Model Architecture
+The model is built upon the **`bert-base-multilingual-cased`** backbone.
+* **Base Model:** BERT (Bidirectional Encoder Representations from Transformers)
+* **Task:** Multi-label Sequence Classification (`BertForSequenceClassification`)
+* **Input Languages:** English, Spanish, French, German, Portuguese
+* **Output:** 8 classification labels (4 Intent labels, 4 Sentiment/Urgency labels). The model is optimized with a Sigmoid activation function and Binary Cross-Entropy Loss to handle independent multi-label prediction.
+* **Max Sequence Length:** 512 tokens
+
+## Intended Use
+* **Automated Customer Service Routing:** Directing messages based on intent (e.g., Fraud alerts go to the security team, Mortgage inquiries go to the loan department).
+* **Prioritization:** Flagging messages with 'Negative' or 'Anxious' sentiment for urgent human intervention.
+* **Financial Market Monitoring:** Analyzing sentiment in multilingual financial news or social media snippets.
+* **Research:** As a strong baseline for transfer learning in related low-resource financial NLP tasks.
+
+## Limitations
+* **Domain Specificity:** The model performs best on messages related to banking, trading, loans, and financial services. Performance degrades on general domain or highly technical domain text (e.g., deep quantitative finance).
+* **Language Scope:** While multilingual, it is limited to the five languages specified in the training set. Performance on other languages is not guaranteed.
+* **Multi-label Ambiguity:** While trained for multi-label, complex messages with mixed intent or complex sarcasm may lead to lower confidence scores.
+
+## Example Code (PyTorch)
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model_name = "Finance/FinBERT-Multilingual-Intent-and-Sentiment"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Example 1: Spanish Complaint
+text_es = "El virement que hice no ha llegado. Es un servicio terrible."
+# Example 2: English Inquiry
+text_en = "I need to increase my credit card limit before I travel next week."
+
+# Process both texts
+inputs = tokenizer([text_es, text_en], return_tensors="pt", padding=True, truncation=True)
+outputs = model(**inputs)
+
+# Get predictions
+logits = outputs.logits
+probabilities = torch.sigmoid(logits)
+
+# Apply threshold (e.g., 0.5) to get multi-label predictions
+threshold = 0.5
+predictions = (probabilities > threshold).int()
+
+# Map predictions back to labels
+labels = model.config.id2label
+predicted_labels = []
+for p in predictions:
+    active_labels = [labels[i] for i, val in enumerate(p) if val == 1]
+    predicted_labels.append(active_labels)
+
+print(f"Spanish Message Labels: {predicted_labels[0]}")
+# Expected output: ['intent: Transfer_Delay_Complaint', 'sentiment: Negative']
+print(f"English Message Labels: {predicted_labels[1]}")
+# Expected output: ['intent: Credit_Limit_Increase_Request', 'sentiment: Neutral']