---
language: en
license: apache-2.0
base_model: distilroberta-base
pipeline_tag: text-classification
tags:
  - distilroberta
  - intent-classification
  - tool-calling
  - screenshots
---

# Screenshot Intent Classifier

This repository contains a DistilRoBERTa-based classifier fine-tuned to decide
**whether a conversational agent should trigger a screenshot tool** for the
latest user message.

## Base Model

This model is fine-tuned from [`distilroberta-base`](https://huggingface.co/distilroberta-base),
and inherits the base encoder's maximum context length and tokenizer.
**distilroberta-base** is a distilled RoBERTa-family encoder intended for faster, cheaper inference than RoBERTa-base, while keeping the standard 512-token context window.

## Classifier

- `0` / `no_screenshot`: do not call the screenshot tool.
- `1` / `take_screenshot`: call the screenshot tool.

The input is a text block representing the recent conversation history,
formatted as one utterance per line (raw user messages separated by newlines),
e.g.:

```text
I'm wondering if blue goes well with yellow.
What's your take on this?
```

At inference time, the host application typically feeds the last few
conversation turns (most importantly the latest user message) in this format
and thresholds the classifier's `take_screenshot` probability to decide
whether to trigger the tool.

## Training Data

The classifier was trained on a curated, hand-labelled private dataset. It
contains hundreds of single-turn and multi-turn examples specifying whether
each user message **should** or **should not** trigger a screenshot, including:

- Clear positive triggers ("look at this", "check this out", "rate this pic").
- Clear negatives (off-topic chit-chat, abstract statements, idioms like
  "I'll look into it").
- Edge cases involving deictic pronouns, quantities ("take 2 screenshots"),
  negation ("don't look"), multi-turn context, and more.

No external user logs or third-party datasets were used; the training data is
purely synthetic / curated for this intent task.

## Training Setup

- Epochs: 4
- Batch size: 32 (per device)
- Learning rate: 5e-05
- Weight decay: 0.01
- Max sequence length: 512

The script builds examples by concatenating conversation history up to and
including the current user message, one utterance per line. Multi-turn
conversations therefore become multiple training examples with growing context.

## Usage

Basic usage with the Transformers library:

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

MODEL_ID = "yapwithai/yap-distilroberta-screenshot-intent"

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

text = "look at this amazing sunset"
inputs = tokenizer(
    text,
    return_tensors="pt",
    truncation=True,
    padding="max_length",
    max_length=512,
)

with torch.no_grad():
    outputs = model(**inputs)
    probs = outputs.logits.softmax(dim=-1)[0]

p_no, p_yes = probs.tolist()
print("P(no_screenshot)=", p_no)
print("P(take_screenshot)=", p_yes)
```

In production, you would:

- Construct a conversation history string similar to the training format
  (recent user turns, each on its own line).
- Run the classifier once per latest user message.
- Threshold `p_yes` to decide whether to trigger the screenshot tool.

## DistilRoBERTa / RoBERTa Citation

If you use DistilRoBERTa in your work, please cite:

```bibtex
@inproceedings{sanh2019distilbert,
  title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
  author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf},
  booktitle={NeurIPS EMC^2 Workshop},
  year={2019}
}

@article{liu2019roberta,
  title={RoBERTa: A Robustly Optimized BERT Pretraining Approach},
  author={Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and Luke Zettlemoyer and Veselin Stoyanov},
  journal={arXiv:1907.11692},
  year={2019}
}
```