All MiniLM L6 V2
The All MiniLM L6 V2 model is a powerful tool for sentence and short paragraph encoding, capable of mapping sentences and paragraphs to a 384-dimensional dense vector space. But how does it work? Essentially, it takes in input text and outputs a vector that captures the semantic information of the input, making it useful for tasks like information retrieval, clustering, and sentence similarity. With its contrastive learning objective and AdamW optimizer, this model achieves state-of-the-art results in these areas. But what really sets it apart is its efficiency - it can handle input text up to 256 word pieces and produces dense vector representations that facilitate efficient and effective text analysis. So, what can you use it for? Try clustering sentences into groups based on their semantic meaning, finding the similarity between sentences, or even generating vector representations of sentences. The possibilities are endless.
Table of Contents
Model Overview
The all-MiniLM-L6-v2 model is a powerful tool for natural language processing tasks. It’s a sentence-transformers model that maps sentences and paragraphs to a 384-dimensional dense vector space. This means it can help with tasks like clustering or semantic search.
Capabilities
The all-MiniLM-L6-v2 model is capable of:
- Taking a sentence or paragraph as input and outputting a vector that captures its semantic information
- Being used for information retrieval, clustering, or sentence similarity tasks
- Handling input text up to 256 word pieces (longer text is truncated)
How it Works
The model uses a contrastive learning objective, which means it’s trained to predict which sentence is paired with a given sentence in the dataset. This approach allows the model to learn a rich representation of language that can be used for a variety of tasks.
Training and Performance
The model was pre-trained on a large dataset using a self-supervised contrastive learning objective and fine-tuned on a 1 billion sentence pairs dataset using a contrastive objective. It was trained on a TPU v3-8 with a batch size of 1024 and a learning rate of 2e-5.
Training Data
The model was trained on a concatenation of multiple datasets, including:
| Dataset | Number of Training Tuples |
|---|---|
| Reddit comments (2015-2018) | 726,484,430 |
| S2ORC Citation pairs (Abstracts) | 116,288,806 |
| WikiAnswers Duplicate question pairs | 77,427,422 |
| … | … |
Performance
The model showcases impressive performance in various tasks, including:
- Clustering similar sentences or paragraphs together
- Searching for semantically similar text
- Information retrieval
- Sentence similarity tasks
Example Use Cases
- Information retrieval: Use the model to retrieve relevant documents or sentences based on their semantic similarity.
- Clustering: Group similar sentences or documents together using the model.
- Sentence similarity: Measure the similarity between two sentences using the model.
Technical Details
- Model architecture: Based on the MiniLM-L6-H384-uncased model
- Training data: Trained on a concatenation of multiple datasets
- Hyperparameters: Trained with a batch size of 1024, learning rate of 2e-5, and sequence length of 128 tokens
Alternatives
- ==BERT==
- ==RoBERTa==
- Other sentence embedding models
Limitations
The model has some limitations, including:
- Limited input length: By default, input text longer than 256 word pieces is truncated.
- Training data bias: The model was trained on a large dataset, but this dataset may still contain biases.
- Lack of contextual understanding: The model may not always understand the context in which a sentence or paragraph is being used.
Getting Started
To use the all-MiniLM-L6-v2 model, you can install the sentence-transformers library and use the following code:
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)
Alternatively, you can use the transformers library and the following code:
from transformers import AutoTokenizer, AutoModel
import torch
sentences = ["This is an example sentence", "Each sentence is converted"]
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
model_output = model(**encoded_input)
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print(sentence_embeddings)
