Create README.md

README.md

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+---
+language: en
+license: apache-2.0
+tags:
+- event-detection
+- token-classification
+- real-time-processing
+- huggingface
+- transformers
+---
+
+# Event Message Detector
+
+## Model Description
+
+The Event Message Detector is a fine-tuned token classification model based on `xlm-roberta-base`. It is designed to process real-time message streams from chat applications (e.g., Slack, IRC) to detect conversations that can be converted into calendar events. The model identifies event-related messages within a sliding window of recent messages, facilitating the extraction of meaningful interactions for scheduling purposes.
+
+## Intended Use
+
+### Direct Use
+
+This model is intended for real-time detection of event-related conversations in multi-user chat environments. It can be integrated into chat applications to automatically identify and extract discussions pertinent to scheduling events, such as meetings or calls.
+
+### Downstream Use
+
+Developers can fine-tune this model further for specific domains or integrate it into larger systems that manage event scheduling, automate calendar entries, or analyze communication patterns.
+
+### Out-of-Scope Use
+
+The model is not designed for general-purpose natural language understanding tasks unrelated to event detection. It should not be used for sentiment analysis, topic modeling, or other unrelated NLP tasks without appropriate fine-tuning.
+
+## Model Details
+
+- **Model Type**: Token Classification
+- **Base Model**: `xlm-roberta-base` (multilingual, 277M parameters)
+- **Training Data**: Labeled chat messages indicating event-related conversations
+- **Training Procedure**: Fine-tuned with a sliding window of 15 messages, using weighted cross-entropy loss
+- **Evaluation Metrics**: ROC-AUC, F1-Score, Precision, Recall
+
+## Usage
+
+```python
+from transformers import AutoModelForTokenClassification, AutoTokenizer
+import torch
+
+# Load model and tokenizer
+model = AutoModelForTokenClassification.from_pretrained("oleksiydolgykh/event-message-detector")
+tokenizer = AutoTokenizer.from_pretrained("oleksiydolgykh/event-message-detector")
+
+# Example message
+message = "[MESSAGE] [user1]: Let's have a meeting tomorrow at 10 AM."
+
+# Tokenize input
+inputs = tokenizer(message, return_tensors="pt")
+
+# Get model predictions
+with torch.no_grad():
+    outputs = model(**inputs)
+
+# Process outputs
+logits = outputs.logits
+predictions = torch.argmax(logits, dim=-1)