a6ddf1bdb4
Model: flax-sentence-embeddings/reddit_single-context_mpnet-base Source: Original Platform
71 lines
3.6 KiB
Markdown
71 lines
3.6 KiB
Markdown
---
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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language: en
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---
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# Model description
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The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised
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contrastive learning objective. We used the pretrained ['mpnet-base'](https://huggingface.co/microsoft/mpnet-base) model and fine-tuned in on a
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700M 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.
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We developped this model during the
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[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
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organized by Hugging Face. We developped this model as part of the project:
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[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well
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as intervention from Google’s Flax, JAX, and Cloud team member about efficient deep learning frameworks.
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## Intended uses
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Our model is intented to be used as a sentence encoder. Given an input sentence, it ouptuts a vector which captures
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the sentence semantic information. The sentence vector may be used for information retrieval, clustering or sentence
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similarity tasks.
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## How to use
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Here is how to use this model to get the features of a given text using [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) library:
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```python
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer('flax-sentence-embeddings/reddit_single-context_mpnet-base')
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text = "Replace me by any text you'd like."
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text_embbedding = model.encode(text)
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# array([-0.01559514, 0.04046123, 0.1317083 , 0.00085931, 0.04585106,
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# -0.05607086, 0.0138078 , 0.03569756, 0.01420381, 0.04266302 ...],
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# dtype=float32)
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```
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# Training procedure
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## Pre-training
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We use the pretrained ['mpnet-base'](https://huggingface.co/microsoft/mpnet-base).
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Please refer to the model card for more detailed information about the pre-training procedure.
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## Fine-tuning
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We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
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We then apply the cross entropy loss by comparing with true pairs.
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### Hyper parameters
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We trained ou model on a TPU v3-8. We train the model during 540k steps using a batch size of 1024 (128 per TPU core).
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We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
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a 2e-5 learning rate. The full training script is accessible in this current repository.
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### Training data
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We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 700M sentences.
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We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.
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We only use the first context response when building the dataset.
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| Dataset | Paper | Number of training tuples |
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|:--------------------------------------------------------:|:----------------------------------------:|:--------------------------:|
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| [Reddit conversationnal](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 |
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