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README.md

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----
-license: apache-2.0
----
+---
+language: en
+license: apache-2.0
+base_model: distilbert/distilbert-base-multilingual-cased
+pipeline_tag: text-classification
+tags:
+  - distilbert_multilingual
+  - intent-classification
+  - tool-calling
+  - screenshots
+---
+
+# Screenshot Intent Classifier
+
+This repository contains a DistilBERT Multilingual-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 [`distilbert/distilbert-base-multilingual-cased`](https://huggingface.co/distilbert/distilbert-base-multilingual-cased),
+and inherits the base encoder's maximum context length and tokenizer.
+**distilbert-base-multilingual-cased** is a distilled version of multilingual BERT, supporting 104 languages with a compact BERT-family encoder and 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: 5
+- Batch size: 16 (per device)
+- Learning rate: 1e-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-distilbert-ml-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.
+
+## DistilBERT Citation
+
+If you use DistilBERT Multilingual 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}
+}
+```