Sentence Transformers
We are forking sentence-transformers/all-MiniLM-L6-v2 as it is similar to the targeting dataset and use case. For more details, please check the pre-trained model weight repository.
- https://huggingface.co./sentence-transformers/all-MiniLM-L6-v2
- Commit Hash: 7dbbc90392e2f80f3d3c277d6e90027e55de9125
Fine-tuning
- Fine-tune the model using a contrastive objective.
- Compute the cosine similarity from each possible sentence pairs from the batch.
- Then apply the cross entropy loss by comparing with true pairs.
Hyper parameters
- Train the model during 100k steps using a batch size of 1024 (128 per TPU core).
- Use a learning rate warm up of 500.
- The sequence length was limited to 128 tokens.
- Used the AdamW optimizer with a 2e-5 learning rate.
- The full training script is accessible in this current repository:
train_script.py.
Performance
| Model Name | Performance Sentence Embeddings (14 Datasets) | Performance Semantic Search (6 Datasets) | Avg. Performance | Speed | Model Size |
|---|---|---|---|---|---|
| all-mpnet-base-v2 | 69.57 | 57.02 | 63.30 | 2800 | 420 MB |
| multi-qa-mpnet-base-dot-v1 | 66.76 | 57.60 | 62.18 | 2800 | 420 MB |
| all-distilroberta-v1 | 68.73 | 50.94 | 59.84 | 4000 | 290 MB |
| all-MiniLM-L12-v2 | 68.70 | 50.82 | 59.76 | 7500 | 120 MB |
| multi-qa-distilbert-cos-v1 | 65.98 | 52.83 | 59.41 | 4000 | 250 MB |
| all-MiniLM-L6-v2 (This model) | 68.06 | 49.54 | 58.80 | 14200 | 80 MB |
| multi-qa-MiniLM-L6-cos-v1 | 64.33 | 51.83 | 58.08 | 14200 | 80 MB |
| paraphrase-multilingual-mpnet-base-v2 | 65.83 | 41.68 | 53.75 | 2500 | 970 MB |
| paraphrase-albert-small-v2 | 64.46 | 40.04 | 52.25 | 5000 | 43 MB |
| paraphrase-multilingual-MiniLM-L12-v2 | 64.25 | 39.19 | 51.72 | 7500 | 420 MB |
| paraphrase-MiniLM-L3-v2 | 62.29 | 39.19 | 50.74 | 19000 | 61 MB |
| distiluse-base-multilingual-cased-v1 | 61.30 | 29.87 | 45.59 | 4000 | 480 MB |
| distiluse-base-multilingual-cased-v2 | 60.18 | 27.35 | 43.77 | 4000 | 480 MB |
Datasets
| 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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