---
license: apache-2.0
datasets:
- jspaulsen/actionable-bert-dataset
base_model:
- answerdotai/ModernBERT-base
---
# Actionable-BERT

A ModernBERT-based binary classifier for determining whether a conversation should be routed to a larger, more capable agent. The model analyzes conversation history and predicts if the user's request requires advanced reasoning or can be handled by a simpler system.

## Model Details

- **Base Model**: [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base)
- **Task**: Binary sequence classification
- **Max Sequence Length**: 8192 tokens
- **Labels**:
  - `0`: Not Actionable (simple request, no routing needed)
  - `1`: Actionable (complex request, route to larger agent)

## Input Format

Conversations are formatted as a single string with turn markers:

```
[U] User message here. [A] Assistant response here. [U] Follow-up user message...
```

- `[U]` marks the start of a user turn
- `[A]` marks the start of an assistant turn
- Turns are concatenated into a single string

## Installation

```bash
pip install transformers torch
```

## Inference Example

```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model_name = "jspaulsen/actionable-bert"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
model.eval()

# Format conversation history
conversation = "[U] Can you help me write a Python function? [A] Sure, what would you like the function to do? [U] I need a recursive function to calculate fibonacci numbers with memoization."

inputs = tokenizer(
    conversation,
    return_tensors="pt",
    truncation=True,
    max_length=8192,
)

with torch.no_grad():
    outputs = model(**inputs)
    prediction = torch.argmax(outputs.logits, dim=-1).item()

labels = {0: "Not Actionable", 1: "Actionable"}
print(f"Prediction: {labels[prediction]}")
```

## Dataset

Trained on [jspaulsen/actionable-bert-dataset](https://huggingface.co/datasets/jspaulsen/actionable-bert-dataset).