README.md

---
pipeline_tag: sentence-similarity
tags:
- ctranslate2
- int8
- float16
  - finetuner
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
datasets:
  - jinaai/negation-dataset
language: en
license: apache-2.0
---
# # Fast-Inference with Ctranslate2
Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU.

quantized version of [jinaai/jina-embedding-t-en-v1](https://huggingface.co/jinaai/jina-embedding-t-en-v1)
```bash
pip install hf-hub-ctranslate2>=2.12.0 ctranslate2>=3.17.1
```

```python
# from transformers import AutoTokenizer
model_name = "michaelfeil/ct2fast-jina-embedding-t-en-v1"
model_name_orig="jinaai/jina-embedding-t-en-v1"

from hf_hub_ctranslate2 import EncoderCT2fromHfHub
model = EncoderCT2fromHfHub(
        # load in int8 on CUDA
        model_name_or_path=model_name,
        device="cuda",
        compute_type="int8_float16"
)
outputs = model.generate(
    text=["I like soccer", "I like tennis", "The eiffel tower is in Paris"],
    max_length=64,
) # perform downstream tasks on outputs
outputs["pooler_output"]
outputs["last_hidden_state"]
outputs["attention_mask"]

# alternative, use SentenceTransformer Mix-In
# for end-to-end Sentence embeddings generation
# (not pulling from this CT2fast-HF repo)

from hf_hub_ctranslate2 import CT2SentenceTransformer
model = CT2SentenceTransformer(
    model_name_orig, compute_type="int8_float16", device="cuda"
)
embeddings = model.encode(
    ["I like soccer", "I like tennis", "The eiffel tower is in Paris"],
    batch_size=32,
    convert_to_numpy=True,
    normalize_embeddings=True,
)
print(embeddings.shape, embeddings)
scores = (embeddings @ embeddings.T) * 100

# Hint: you can also host this code via REST API and
# via github.com/michaelfeil/infinity  


```

Checkpoint compatible to [ctranslate2>=3.17.1](https://github.com/OpenNMT/CTranslate2)
and [hf-hub-ctranslate2>=2.12.0](https://github.com/michaelfeil/hf-hub-ctranslate2)
- `compute_type=int8_float16` for `device="cuda"`
- `compute_type=int8`  for `device="cpu"`

Converted on 2023-10-13 using
```
LLama-2 -> removed <pad> token.
```

# Licence and other remarks:
This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo.

# Original description
    

<br><br>

<p align="center">
<img src="https://github.com/jina-ai/finetuner/blob/main/docs/_static/finetuner-logo-ani.svg?raw=true" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px">
</p>


<p align="center">
<b>The text embedding set trained by <a href="https://jina.ai/"><b>Jina AI</b></a>, <a href="https://github.com/jina-ai/finetuner"><b>Finetuner</b></a> team.</b>
</p>


## Intented Usage & Model Info

`jina-embedding-t-en-v1` is a tiny language model that has been trained using Jina AI's Linnaeus-Clean dataset.
This dataset consists of 380 million pairs of sentences, which include both query-document pairs.
These pairs were obtained from various domains and were carefully selected through a thorough cleaning process.
The Linnaeus-Full dataset, from which the Linnaeus-Clean dataset is derived, originally contained 1.6 billion sentence pairs.

The model has a range of use cases, including information retrieval, semantic textual similarity, text reranking, and more.

With a tiny small parameter size of just 14 million parameters,
the model enables lightning-fast inference on CPU, while still delivering impressive performance.
Additionally, we provide the following options:

- [`jina-embedding-t-en-v1`](https://huggingface.co/jinaai/jina-embedding-t-en-v1): 14 million parameters **(you are here)**.
- [`jina-embedding-s-en-v1`](https://huggingface.co/jinaai/jina-embedding-s-en-v1): 35 million parameters.
- [`jina-embedding-b-en-v1`](https://huggingface.co/jinaai/jina-embedding-b-en-v1): 110 million parameters.
- [`jina-embedding-l-en-v1`](https://huggingface.co/jinaai/jina-embedding-l-en-v1): 330 million parameters.
- `jina-embedding-1b-en-v1`: 1.2 billion parameters, 10 times bert-base (soon).
- `jina-embedding-6b-en-v1`: 6 billion parameters, 30 times bert-base (soon).

## Data & Parameters

Please checkout our [technical blog](https://arxiv.org/abs/2307.11224).

## Metrics

We compared the model against `all-minilm-l6-v2`/`all-mpnet-base-v2` from sbert and `text-embeddings-ada-002` from OpenAI:

|Name|param    |dimension|
|------------------------------|-----|------|
|all-minilm-l6-v2|23m      |384|
|all-mpnet-base-v2 |110m     |768|
|ada-embedding-002|Unknown/OpenAI API  |1536|
|jina-embedding-t-en-v1|14m      |312|
|jina-embedding-s-en-v1|35m      |512|
|jina-embedding-b-en-v1|110m      |768|
|jina-embedding-l-en-v1|330m      |1024|


|Name|STS12|STS13|STS14|STS15|STS16|STS17|TRECOVID|Quora|SciFact|
|------------------------------|-----|-----|-----|-----|-----|-----|--------|-----|-----|
|all-minilm-l6-v2|0.724|0.806|0.756|0.854|0.79 |0.876|0.473   |0.876|0.645  |
|all-mpnet-base-v2|0.726|**0.835**|0.78 |0.857|0.8  |**0.906**|0.513   |0.875|0.656  |
|ada-embedding-002|0.698|0.833|0.761|0.861|**0.86** |0.903|**0.685**   |0.876|**0.726**  |
|jina-embedding-t-en-v1|0.717|0.773|0.731|0.829|0.777|0.860|0.482   |0.840|0.522  |
|jina-embedding-s-en-v1|0.743|0.786|0.738|0.837|0.80|0.875|0.523   |0.857|0.524  |
|jina-embedding-b-en-v1|**0.751**|0.809|0.761|0.856|0.812|0.890|0.606   |0.876|0.594  |
|jina-embedding-l-en-v1|0.745|0.832|**0.781**|**0.869**|0.837|0.902|0.573   |**0.881**|0.598  |

## Inference Speed

We encoded a single sentence "What is the current weather like today?" 10k times on:

1. cpu: MacBook Pro 2020, 2 GHz Quad-Core Intel Core i5
2. gpu: 1 Nvidia 3090

And recorded time spent to demonstrate the embedding speed:

|Name|param    |dimension| time@cpu | time@gpu |
|------------------------------|-----|------|-----|-----|
|jina-embedding-t-en-v1|14m      |312| 5.78s | 2.36s|
|all-minilm-l6-v2|23m      |384| 11.95s | 2.70s |
|jina-embedding-s-en-v1|35m      |512| 17.25s | 2.81s |


## Usage

Use with Jina AI Finetuner

```python
!pip install finetuner
import finetuner

model = finetuner.build_model('jinaai/jina-embedding-t-en-v1')
embeddings = finetuner.encode(
    model=model,
    data=['how is the weather today', 'What is the current weather like today?']
)
print(finetuner.cos_sim(embeddings[0], embeddings[1]))
```

Use with sentence-transformers:

```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim

sentences = ['how is the weather today', 'What is the current weather like today?']

model = SentenceTransformer('jinaai/jina-embedding-t-en-v1')
embeddings = model.encode(sentences)
print(cos_sim(embeddings[0], embeddings[1]))
```

## Fine-tuning

Please consider [Finetuner](https://github.com/jina-ai/finetuner).

## Plans

1. The development of `jina-embedding-s-en-v2` is currently underway with two main objectives: improving performance and increasing the maximum sequence length.
2. We are currently working on a bilingual embedding model that combines English and X language. The upcoming model will be called `jina-embedding-s/b/l-de-v1`.

## Contact

Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.

## Citation

If you find Jina Embeddings useful in your research, please cite the following paper:

``` latex
@misc{günther2023jina,
      title={Jina Embeddings: A Novel Set of High-Performance Sentence Embedding Models}, 
      author={Michael Günther and Louis Milliken and Jonathan Geuter and Georgios Mastrapas and Bo Wang and Han Xiao},
      year={2023},
      eprint={2307.11224},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```