---
license: mit
language:
- en
license_link: https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE
base_model: 
  - BAAI/bge-base-en-v1.5
---
# bge-base-en-v1.5-int8-ov

 * Model creator: [BAAI](https://huggingface.co/BAAI)
 * Original model: [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)

## Description
This is [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2025/documentation/openvino-ir-format.html) (Intermediate Representation) format with quantization to INT8 by [NNCF](https://github.com/openvinotoolkit/nncf).

**Disclaimer**: Model is provided as a preview and may be update in the future.


## Quantization Parameters

Weight compression was performed using `nncf.compress_weights` with the following parameters:

* mode: **INT8_ASYM**

For more information on quantization, check the [OpenVINO model optimization guide](https://docs.openvino.ai/2025/openvino-workflow/model-optimization-guide/weight-compression.html).


## Compatibility

The provided OpenVINO™ IR model is compatible with:

* OpenVINO version 2025.3.0 and higher
* Optimum Intel 1.25.2 and higher


## Running Model Inference with [Optimum Intel](https://huggingface.co/docs/optimum/intel/index)

1. Install packages required for using [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) integration with the OpenVINO backend:

```
pip install optimum[openvino]
```

2. Run model inference:

```
import torch
from transformers import AutoTokenizer

from optimum.intel.openvino import OVModelForFeatureExtraction


# Sentences we want sentence embeddings for
sentences = ["Sample Data-1", "Sample Data-2"]

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('OpenVINO/bge-base-en-v1.5-int8-ov')
model = OVModelForFeatureExtraction.from_pretrained('OpenVINO/bge-base-en-v1.5-int8-ov')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
model_output = model(**encoded_input)

# Perform pooling. In this case, cls pooling.
sentence_embeddings = model_output[0][:, 0]

# normalize embeddings
sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:", sentence_embeddings)
```

For more examples and possible optimizations, refer to the [Inference with Optimum Intel](https://docs.openvino.ai/2025/openvino-workflow-generative/inference-with-optimum-intel.html).

You can find more detailed usage examples in OpenVINO Notebooks:

- [RAG text generation](https://openvinotoolkit.github.io/openvino_notebooks/?search=RAG+system)

## Limitations

Check the original [model card](https://huggingface.co/BAAI/bge-base-en-v1.5) for limitations.

## Legal information

The original model is distributed under [MIT](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE) license. More details can be found in [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5).

## Disclaimer

Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See [Intel’s Global Human Rights Principles](https://www.intel.com/content/dam/www/central-libraries/us/en/documents/policy-human-rights.pdf). Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.