The total number of sentence pairs is above 1 billion sentences. We use the concatenation from multiple datasets to fine-tune our model. The full training script is accessible in this current repository: train_script.py. We used the AdamW optimizer withĪ 2e-5 learning rate. The sequence length was limited to 128 tokens. We train the model during 100k steps using a batch size of 1024 (128 per TPU core). We then apply the cross entropy loss by comparing with true pairs. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We fine-tune the model using a contrastive objective. Please refer to the model card for more detailed information about the pre-training procedure. We use the pretrained nreimers/MiniLM-L6-H384-uncased model. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.īy default, input text longer than 256 word pieces is truncated. Given an input text, it outputs a vector which captures Our model is intended to be used as a sentence and short paragraph encoder. 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. Train the Best Sentence Embedding Model Ever with 1B Training Pairs. We developed this model as part of the project: 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.Ĭommunity week using JAX/Flax for NLP & CV, We used the pretrained nreimers/MiniLM-L6-H384-uncased model and fine-tuned in on aġB sentence pairs dataset. The project aims to train sentence embedding models on very large sentence level datasets using a self-supervisedĬontrastive learning objective. Sentence_embeddings = F.normalize(sentence_embeddings, p= 2, dim= 1)įor an automated evaluation of this model, see the Sentence Embeddings Benchmark: Sentence_embeddings = mean_pooling(model_output, encoded_input) # Compute token embeddings with torch.no_grad(): Model = om_pretrained( 'sentence-transformers/all-MiniLM-L6-v2')Įncoded_input = tokenizer(sentences, padding= True, truncation= True, return_tensors= 'pt') Tokenizer = om_pretrained( 'sentence-transformers/all-MiniLM-L6-v2') # Sentences we want sentence embeddings for sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded. Input_mask_expanded = attention_mask.unsqueeze(- 1).expand(token_embeddings.size()). Token_embeddings = model_output #First element of model_output contains all token embeddings #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling( model_output, attention_mask): from transformers import AutoTokenizer, AutoModel 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. Model = SentenceTransformer( 'sentence-transformers/all-MiniLM-L6-v2') Then you can use the model like this: from sentence_transformers import SentenceTransformer Using this model becomes easy when you have sentence-transformers installed: pip install -U sentence-transformers 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.
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