pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity language: en license: apache-2.0
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Using this model becomes easy when you have sentence-transformers installed:
pip install -U sentence-transformers
Then you can use the model like this:
from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('obrizum/all-MiniLM-L6-v2') embeddings = model.encode(sentences) print(embeddings)
Without sentence-transformers, 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.
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 #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('obrizum/all-MiniLM-L6-v2') model = AutoModel.from_pretrained('obrizum/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)
For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net
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 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 developped this model during the Community week using JAX/Flax for NLP & CV, organized by Hugging Face. We developped 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 Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts 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.
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.
We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs.
We trained ou model on a TPU v3-8. We train the model during 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:
We use the concatenation from 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 which configuration is detailed in the
|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|
|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|
|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|
|Quora Question Triplets||-||103,663|
|Natural Questions (NQ)||paper||100,231|