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--- |
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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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- transformers |
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datasets: |
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- wikipedia |
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- bookcorpus |
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- ms_marco |
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- BeIR/fiqa |
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- BeIR/trec-covid |
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- BeIR/scifact |
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- BeIR/nfcorpus |
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- BeIR/nq |
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- BeIR/hotpotqa |
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- BeIR/arguana |
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- BeIR/webis-touche2020 |
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- BeIR/quora |
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- BeIR/dbpedia-entity |
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- BeIR/scidocs |
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- BeIR/fever |
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- BeIR/climate-fever |
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--- |
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# nthakur/RetroMAE_BEIR |
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This is a port of the [RetroMAE_BEIR](https://huggingface.co/Shitao/RetroMAE_BEIR) Model to [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. |
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<!--- Describe your model here --> |
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## Usage (Sentence-Transformers) |
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Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: |
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``` |
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pip install -U sentence-transformers |
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``` |
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Then you can use the model like this: |
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```python |
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from sentence_transformers import SentenceTransformer |
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sentences = ["This is an example sentence", "Each sentence is converted"] |
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model = SentenceTransformer('nthakur/RetroMAE_BEIR') |
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embeddings = model.encode(sentences) |
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print(embeddings) |
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``` |
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## Usage (HuggingFace Transformers) |
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Without [sentence-transformers](https://www.SBERT.net), 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. |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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import torch |
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def cls_pooling(model_output, attention_mask): |
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return model_output[0][:,0] |
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# Sentences we want sentence embeddings for |
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sentences = ['This is an example sentence', 'Each sentence is converted'] |
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# Load model from HuggingFace Hub |
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tokenizer = AutoTokenizer.from_pretrained('nthakur/RetroMAE_BEIR') |
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model = AutoModel.from_pretrained('nthakur/RetroMAE_BEIR') |
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# Tokenize sentences |
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encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') |
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# Compute token embeddings |
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with torch.no_grad(): |
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model_output = model(**encoded_input) |
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# Perform pooling. In this case, cls pooling. |
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sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask']) |
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print("Sentence embeddings:") |
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print(sentence_embeddings) |
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``` |
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## Evaluation Results |
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<!--- Describe how your model was evaluated --> |
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For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=nthakur/RetroMAE_BEIR) |
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## Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) |
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) |
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``` |
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## Citing & Authors |
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Have a look at [RetroMAE](https://github.com/staoxiao/RetroMAE/). |
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<!--- Describe where people can find more information --> |
