
Fork of [thenlper/gte-small](huggingface.co/thenlper/gte-small) with ONNX to work with Transformers.js.

---

# gte-small

General Text Embeddings (GTE) model. [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://arxiv.org/abs/2308.03281)

The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including [GTE-large](https://huggingface.co/thenlper/gte-large), [GTE-base](https://huggingface.co/thenlper/gte-base), and [GTE-small](https://huggingface.co/thenlper/gte-small). The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc.

## Metrics

Performance of the GTE models compared with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard).



| Model Name | Model Size (GB) | Dimension | Sequence Length | Average (56) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) | Classification (12) |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| [**gte-large**](https://huggingface.co/thenlper/gte-large) | 0.67 | 1024 | 512 | **63.13** | 46.84 | 85.00 | 59.13 | 52.22 | 83.35 | 31.66 | 73.33 |
| [**gte-base**](https://huggingface.co/thenlper/gte-base) 	| 0.22 | 768 | 512 | **62.39** | 46.2 | 84.57 | 58.61 | 51.14 | 82.3 | 31.17 | 73.01 |
| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1.34 | 1024| 512 | 62.25 | 44.49 | 86.03 | 56.61 | 50.56 | 82.05 | 30.19 | 75.24 |
| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.44 | 768 | 512 | 61.5 | 43.80 | 85.73 | 55.91 | 50.29 | 81.05 | 30.28 | 73.84 |
| [**gte-small**](https://huggingface.co/thenlper/gte-small) | 0.07 | 384 | 512 | **61.36** | 44.89 | 83.54 | 57.7 | 49.46 | 82.07 | 30.42 | 72.31 |
| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | - | 1536 | 8192 | 60.99 | 45.9 | 84.89 | 56.32 | 49.25 | 80.97 | 30.8 | 70.93 |
| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.13 | 384 | 512 | 59.93 | 39.92 | 84.67 | 54.32 | 49.04 | 80.39 | 31.16 | 72.94 |
| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 9.73 | 768 | 512 | 59.51 | 43.72 | 85.06 | 56.42 | 42.24 | 82.63 | 30.08 | 73.42 |
| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) 	| 0.44 | 768 | 514 	| 57.78 | 43.69 | 83.04 | 59.36 | 43.81 | 80.28 | 27.49 | 65.07 |
| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) 	| 28.27 | 4096 | 2048 | 57.59 | 38.93 | 81.9 | 55.65 | 48.22 | 77.74 | 33.6 | 66.19 |
| [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) 	| 0.13 | 384 | 512 	| 56.53 | 41.81 | 82.41 | 58.44 | 42.69 | 79.8 | 27.9 | 63.21 |
| [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) 	| 0.09 | 384 | 512 	| 56.26 | 42.35 | 82.37 | 58.04 | 41.95 | 78.9 | 30.81 | 63.05 |
| [contriever-base-msmarco](https://huggingface.co/nthakur/contriever-base-msmarco) 	| 0.44 | 768 | 512 	| 56.00 | 41.1 	| 82.54 | 53.14 | 41.88 | 76.51 | 30.36 | 66.68 |
| [sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) 	| 0.22 | 768 | 512 	| 55.27 | 40.21 | 85.18 | 53.09 | 33.63 | 81.14 | 31.39 | 69.81 |


## Usage

### Deno

```javascript
import { env, pipeline } from "https://cdn.jsdelivr.net/npm/@xenova/transformers@2.5.0";

// Some config for Deno
env.useBrowserCache = false;
env.allowLocalModels = false;

// Give it any input you want
const input = "Hello AI";

// Create the pipeline
const pipe = await pipeline(
  "feature-extraction",
  "koxy-ai/gte-small"
);

// Generate the embedding
const output = await pipe(input, {
  pooling: "mean",
  normalize: true
});

// Extract the embedding from the output
const embedding = Array.from(output.data);

// Do anything with the embedding
console.log(embedding);
```

### Browser
Using Javascript modules.
```javascript
<script type="module">

import { pipeline } from "https://cdn.jsdelivr.net/npm/@xenova/transformers@2.5.0";

// Create the pipeline
const setPipe = async () => {
  return await pipeline(
    "feature-extraction",
    "koxy-ai/gte-small"
  );
};

const generateEmbedding = async (input) => {
  const pipe = await setPipe();
  const output = await pipe(input, {
    pooling: "mean",
    normalize: true
  });
  return Array.from(output.data);
};

export default generateEmbedding;

</script>
```

### Node JS

```bash
npm i @xenova/transformers
```

```javascript
import { pipeline } from "@xenova/transformers";

(async () => {
  // Give it any input you want
  const input = "Hello AI";

  // Create the pipeline
  const pipe = await pipeline(
    "feature-extraction",
    "koxy-ai/gte-small"
  );

  // Generate the embedding
  const output = await pipe(input, {
    pooling: "mean",
    normalize: true
  });

  // Extract the embedding from the output
  const embedding = Array.from(output.data);

  // Do anything with the embedding
  console.log(embedding);
})();
```

### Limitation

This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens.

### Citation

```
@misc{li2023general,
      title={Towards General Text Embeddings with Multi-stage Contrastive Learning}, 
      author={Zehan Li and Xin Zhang and Yanzhao Zhang and Dingkun Long and Pengjun Xie and Meishan Zhang},
      year={2023},
      eprint={2308.03281},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```
{"language": ["en"], "license": "mit", "library_name": "transformers.js", "tags": ["Transformers.js", "feature extraction"], "pipeline_tag": "feature-extraction"}
transformers.js onnx bert feature-extraction Transformers.js feature extraction en arxiv:2308.03281 license:mit region:us