Upload WebBERT v2 action classifier (ONNX + tokenizer + classes)

README.md

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+---
+license: apache-2.0
+library_name: onnx
+tags:
+  - onnx
+  - distilbert
+  - text-classification
+  - browser-automation
+  - web-navigation
+pipeline_tag: text-classification
+datasets:
+  - custom
+metrics:
+  - accuracy
+  - f1
+model-index:
+  - name: webbert-action-classifier
+    results:
+      - task:
+          type: text-classification
+          name: Web Action Classification
+        metrics:
+          - name: Accuracy
+            type: accuracy
+            value: 0.909
+          - name: Macro F1
+            type: f1
+            value: 0.909
+---
+
+# WebBERT Action Classifier
+
+DistilBERT-based action classifier for web browser navigation. Given a task goal, page elements, and domain, predicts the next browser action.
+
+## Model Details
+
+- **Base model:** distilbert-base-uncased
+- **Fine-tuned on:** 9,025 synthetic + hard-case examples
+- **Classes:** 15 web action types
+- **Input format:** `[TASK] goal [ELEMENTS] label:type @(cx,cy) ... [PAGE] domain`
+- **Max sequence length:** 256
+- **Export format:** ONNX (opset 14)
+
+## Classes
+
+click, type, scroll_down, scroll_up, wait, go_back, skip, extract_content, dismiss_popup, accept_cookies, fill_form, submit_form, click_next, download, select_dropdown
+
+## Performance
+
+| Metric | Value |
+|--------|-------|
+| Overall Accuracy | 90.9% |
+| Macro F1 | 0.909 |
+| Typical scenarios | 92.0% |
+| Complex edge cases | 89.5% |
+| Inference latency (CPU) | ~5ms |
+| Model size | ~256 MB |
+
+## Usage
+
+### Python (ONNX Runtime)
+
+```python
+import onnxruntime as ort
+from tokenizers import Tokenizer
+
+session = ort.InferenceSession("webbert.onnx")
+tokenizer = Tokenizer.from_file("webbert-tokenizer.json")
+tokenizer.enable_padding(length=256, pad_id=0, pad_token="[PAD]")
+tokenizer.enable_truncation(max_length=256)
+
+text = "[TASK] click login button [ELEMENTS] Login:button @(0.50,0.30) [PAGE] example.com"
+encoding = tokenizer.encode(text)
+
+import numpy as np
+input_ids = np.array([encoding.ids], dtype=np.int64)
+attention_mask = np.array([encoding.attention_mask], dtype=np.int64)
+
+outputs = session.run(None, {"input_ids": input_ids, "attention_mask": attention_mask})
+pred = np.argmax(outputs[0], axis=-1)[0]
+```
+
+### Rust (ort + tokenizers)
+
+Used in [nyaya-agent](https://github.com/biztiger/nyaya-agent) as Layer 2 in the browser navigation cascade.
+
+## Files
+
+- `webbert.onnx` — ONNX model (DistilBERT fine-tuned, ~256 MB)
+- `webbert-tokenizer.json` — HuggingFace tokenizer (single JSON file)
+- `webbert-classes.json` — Ordered class label list
+
+## Training
+
+Trained with HuggingFace Transformers on 9,025 examples (6,000 base + 3,025 hard-case disambiguation). 5 epochs, lr=2e-5, batch_size=32, warmup_steps=100.

webbert-classes.json

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+[
+  "click",
+  "type",
+  "scroll_down",
+  "scroll_up",
+  "wait",
+  "go_back",
+  "skip",
+  "extract_content",
+  "dismiss_popup",
+  "accept_cookies",
+  "fill_form",
+  "submit_form",
+  "click_next",
+  "download",
+  "select_dropdown"
+]

webbert.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:06a9e29ad2e3f40702f373ab1867e5958cce2232c6b6b8bf6f3123a4b6d8a793
+size 267999393