---
license: apache-2.0
base_model: HuggingFaceTB/SmolLM-360M
tags:
- text-generation
- entity-extraction
- calendar-events
- smollm
- fine-tuned
- nlp
language:
- en
metrics:
- f1
- accuracy
library_name: transformers
pipeline_tag: text-generation
---

# SmolLM-360M Fine-tuned for Calendar Event Entity Extraction


    SmolLM-360M fine-tuned for calendar event entity extraction.
    Extracts structured information from natural language event descriptions.
    

## Model Details

- **Base Model:** HuggingFaceTB/SmolLM-360M
- **Model Size:** 360M parameters
- **Task:** Calendar event entity extraction
- **Language:** English
- **License:** Apache 2.0

## Supported Entity Fields

This model extracts the following entities from calendar event descriptions:

- **action**: The main activity or event type
- **date**: Event date
- **time**: Event time
- **attendees**: List of people attending
- **location**: Event location
- **duration**: Event duration
- **recurrence**: Recurrence pattern (if any)
- **notes**: Additional notes

## Performance Metrics

### Fine-tuned Model Performance

- **Exact Match Accuracy:** 0.6625
- **Macro F1 Score:** 0.7466
- **Macro Precision:** 0.7476
- **Macro Recall:** 0.7457

### Improvement over Baseline

- **F1 Score Improvement:** +0.7488
- **Accuracy Improvement:** +0.6750

## Usage

### Quick Start

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import json

# Load model and tokenizer
model_name = "smollm-360m-event-extraction"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Example event text
event_text = "Team meeting tomorrow at 2pm with John and Sarah for 1 hour"

# Create prompt
prompt = f'''Extract the following entities from the calendar event description:

Event: {event_text}

Please provide the extracted information in this exact JSON format:
{{
    "action": "extracted action or null",
    "date": "extracted date or null", 
    "time": "extracted time or null",
    "attendees": ["list of attendees"] or null,
    "location": "extracted location or null",
    "duration": "extracted duration or null", 
    "recurrence": "extracted recurrence or null",
    "notes": "extracted notes or null"
}}

Extracted entities:'''

# Tokenize and generate
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.1)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

# Extract and parse JSON
generated_json = response[len(prompt):].strip()
entities = json.loads(generated_json)
print(entities)
```

### Batch Processing

```python
def extract_entities_batch(event_texts, model, tokenizer):
    results = []
    for event_text in event_texts:
        # Use the same prompt format as above
        # ... (generation code)
        results.append(entities)
    return results
```

## Training Details

### Training Data

- **Dataset Size:** 793 examples (based on provided JSONL file)
- **Data Split:** 80.0% train, 10.0% validation, 10.0% test
- **Data Format:** Instruction-following format with JSON output

### Training Configuration

- **Learning Rate:** 2e-05
- **Training Epochs:** 3
- **Batch Size:** 2
- **Max Sequence Length:** 256
- **Optimizer:** AdamW with weight decay
- **Learning Rate Schedule:** Linear warmup

### Training Infrastructure

- **Framework:** Hugging Face Transformers + Accelerate
- **Hardware:** GPU-optimized (also supports CPU)
- **Mixed Precision:** FP16 training enabled

## Example Outputs

### Input
```
"Team meeting tomorrow at 2pm with John and Sarah for 1 hour"
```

### Output
```json
{
    "action": "Team meeting",
    "date": "tomorrow",
    "time": "2pm", 
    "attendees": ["John", "Sarah"],
    "location": null,
    "duration": "1 hour",
    "recurrence": null,
    "notes": null
}
```

## Limitations and Biases

- The model is trained on English calendar events only
- Performance may vary on events with complex or unusual phrasing
- Date/time formats should follow common conventions for best results
- The model may struggle with implicit information not stated in the text

## Ethical Considerations

- This model is designed for calendar event processing and should not be used for sensitive personal data without proper privacy safeguards
- Users should validate outputs, especially for critical scheduling applications
- The model may reflect biases present in the training data