đ all-MiniLM-L6-v2
This is a sentence-transformers model that maps sentences and paragraphs to a 384-dimensional dense vector space. It can be used for tasks such as clustering or semantic search.
đ Quick Start
⨠Features
- Maps sentences & paragraphs to a 384-dimensional dense vector space.
- Suitable for tasks like clustering and semantic search.
đĻ Installation
Using this model becomes easy when you have sentence-transformers installed. You can install it with the following command:
pip install -U sentence-transformers
đģ Usage Examples
Basic Usage
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)
Advanced Usage
Without sentence-transformers, you can use the model as follows. First, pass your input through the transformer model, then apply the right pooling-operation on top of the contextualized word embeddings.
from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn.functional as F
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
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 = ['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'])
sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:")
print(sentence_embeddings)
đ Documentation
Evaluation Results
For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net
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
model and fine-tuned it 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, organized by Hugging Face. We developed this model as part of the project: Train the Best Sentence Embedding Model Ever with 1B Training Pairs. We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3 - 8, as well as intervention from Google's Flax, JAX, and Cloud team members 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 that 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
model. Please refer to the model card for more detailed information about the pre - training procedure.
Fine - tuning
We fine - tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pair from the batch. Then we apply the cross - entropy loss by comparing with true pairs.
Hyper parameters
We trained our model on a TPU v3 - 8. We train the model for 100k steps using a batch size of 1024 (128 per TPU core). We use a learning rate warm - up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e - 5 learning rate. The full training script is accessible in this current repository: train_script.py
.
Training data
We use the concatenation of multiple datasets to fine - tune our model. The total number of sentence pairs is above 1 billion sentences. We sampled each dataset given a weighted probability, and the configuration is detailed in the data_config.json
file.
Property |
Details |
Model Type |
Sentence embedding model |
Training Data |
Concatenation of multiple datasets including Reddit comments (2015 - 2018), S2ORC, WikiAnswers, PAQ, etc. |
Dataset |
Paper |
Number of training tuples |
Reddit comments (2015-2018) |
paper |
726,484,430 |
S2ORC Citation pairs (Abstracts) |
paper |
116,288,806 |
WikiAnswers Duplicate question pairs |
paper |
77,427,422 |
PAQ (Question, Answer) pairs |
paper |
64,371,441 |
S2ORC Citation pairs (Titles) |
paper |
52,603,982 |
S2ORC (Title, Abstract) |
paper |
41,769,185 |
Stack Exchange (Title, Body) pairs |
- |
25,316,456 |
Stack Exchange (Title+Body, Answer) pairs |
- |
21,396,559 |
Stack Exchange (Title, Answer) pairs |
- |
21,396,559 |
MS MARCO triplets |
paper |
9,144,553 |
GOOAQ: Open Question Answering with Diverse Answer Types |
paper |
3,012,496 |
Yahoo Answers (Title, Answer) |
paper |
1,198,260 |
Code Search |
- |
1,151,414 |
COCO Image captions |
paper |
828,395 |
SPECTER citation triplets |
paper |
684,100 |
Yahoo Answers (Question, Answer) |
paper |
681,164 |
Yahoo Answers (Title, Question) |
paper |
659,896 |
SearchQA |
paper |
582,261 |
Eli5 |
paper |
325,475 |
Flickr 30k |
paper |
317,695 |
Stack Exchange Duplicate questions (titles) |
|
304,525 |
AllNLI (SNLI and MultiNLI |
paper SNLI, paper MultiNLI |
277,230 |
Stack Exchange Duplicate questions (bodies) |
|
250,519 |
Stack Exchange Duplicate questions (titles+bodies) |
|
250,460 |
Sentence Compression |
paper |
180,000 |
Wikihow |
paper |
128,542 |
Altlex |
paper |
112,696 |
Quora Question Triplets |
- |
103,663 |
Simple Wikipedia |
paper |
102,225 |
Natural Questions (NQ) |
paper |
100,231 |
SQuAD2.0 |
paper |
87,599 |
TriviaQA |
- |
73,346 |
Total |
|
1,170,060,424 |
đ License
This project is licensed under the Apache - 2.0 license.