sentence-transformers/all-MiniLM-L6-v2

Model card

Embeddings model on the open range. Tagged sentence-transformers, pytorch, rust, onnx. Languages: tf, en. Pull it straight from sentence-transformers/all-MiniLM-L6-v2.

# all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.

## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
embeddings = model.encode(sentences)
print(embeddings)
```

## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

```python
from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn.functional as F

#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)


# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')

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

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

# Normalize embeddings
sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)

print("Sentence embeddings:")
print(sentence_embeddings)
```

------

## Background

The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised 
contrastive learning objective. We used the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and fine-tuned in on a 
1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.

We developed this model during the 
[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), 
organized by Hugging Face. We developed this model as part of the project:
[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.

## Intended uses

Our model is intended to be used as a sentence and short paragraph encoder. Given an input text, it outputs a vector which captures 
the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.

By default, input text longer than 256 word pieces is truncated.


## Training procedure

### Pre-training 

We use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model. Please refer to t

Install it

# python
pip install -U huggingface_hub
huggingface-cli download sentence-transformers/all-MiniLM-L6-v2

# or in code
from transformers import pipeline
pipe = pipeline(model="sentence-transformers/all-MiniLM-L6-v2")

Files and versions

• .gitattributes
• 1_Pooling/config.json
• README.md
• config.json
• config_sentence_transformers.json
• data_config.json
• model.safetensors
• modules.json
• onnx/model.onnx
• onnx/model_O1.onnx
• onnx/model_O2.onnx
• onnx/model_O3.onnx
• onnx/model_O4.onnx
• onnx/model_qint8_arm64.onnx
• onnx/model_qint8_avx512.onnx
• onnx/model_qint8_avx512_vnni.onnx
• onnx/model_quint8_avx2.onnx
• openvino/openvino_model.bin
• openvino/openvino_model.xml
• openvino/openvino_model_qint8_quantized.bin
• openvino/openvino_model_qint8_quantized.xml
• pytorch_model.bin
• rust_model.ot
• sentence_bert_config.json
• special_tokens_map.json
• tf_model.h5
• tokenizer.json
• tokenizer_config.json
• train_script.py
• vocab.txt