sentence-transformers/all-MiniLM-L6-v2
We present a sentence transformation model that achieves state-of-the-art results on various NLP tasks without requiring task-specific architectures or fine-tuning. Our approach leverages contrastive learning and utilizes a variety of datasets to learn robust sentence representations. We evaluate our model on several benchmarks and demonstrate its effectiveness in various applications such as text classification, sentiment analysis, named entity recognition, and question answering.
We present a sentence transformation model that achieves state-of-the-art results on various NLP tasks without requiring task-specific architectures or fine-tuning. Our approach leverages contrastive learning and utilizes a variety of datasets to learn robust sentence representations. We evaluate our model on several benchmarks and demonstrate its effectiveness in various applications such as text classification, sentiment analysis, named entity recognition, and question answering.
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inputs
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whether to normalize the computed embeddings 2
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all-MiniLM-L6-v2
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.
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 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.
Intended uses
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.
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 pairs from the batch. We then apply the cross entropy loss by comparing with true pairs.
Hyper parameters
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: train_script.py.
Training data
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 data_config.json file.
| 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 |
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