---
language: en
tags:
  - sentence-transformers
  - web-agent
  - bi-encoder
  - element-selection
  - mind2web
license: apache-2.0
datasets:
  - osunlp/Mind2Web
pipeline_tag: feature-extraction
---

# Web Agent Bi-Encoder (v1)

A fine-tuned bi-encoder for web element selection, trained on the Mind2Web dataset.

## What this model does

Given a natural-language task description (e.g., "click the search button") and a set
of serialized web page elements, this model identifies the correct element to interact with.

## Training

- **Base model**: BAAI/bge-small-en-v1.5
- **Training data**: Mind2Web (~5825 examples)
- **Loss**: MultipleNegativesRankingLoss
- **Format version**: v1

## Evaluation

- **Top-1 accuracy**: 80.4%
- **Δ vs zero-shot baseline**: +13.8 points
- **Baseline**: BAAI/bge-small-en-v1.5

## Serialization format

This model expects inputs serialized in the web-agent v1 format. See the
repository's `docs/spec/03-node-serialization.md` for the full specification.
Models trained on a different format version are incompatible.

## Limitations

- Trained primarily on English-language web tasks
- US-centric site distribution in Mind2Web
- Dataset collected circa 2023; some site layouts may have changed
- Best for common web interaction patterns (forms, search, navigation)

## Usage

```python
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("doeve/web-agent-bge-small-v1", revision="v1.0.0")
query_emb = model.encode(["search for flights"])
candidate_embs = model.encode(["textbox \"Search\" | — | in form:\"\", ..."])
```

Or via Transformers.js in the browser:

```javascript
import { pipeline } from "@huggingface/transformers";
const extractor = await pipeline("feature-extraction", "doeve/web-agent-bge-small-v1",
  { revision: "v1.0.0", dtype: "q8" });
```