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Browse files- .gitattributes +3 -0
- README.md +54 -0
- config.json +25 -0
- pytorch_model.onnx +3 -0
- sentencepiece.bpe.model +3 -0
- special_tokens_map.json +9 -0
- tokenizer.json +3 -0
- tokenizer_config.json +20 -0
.gitattributes
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*.onnx filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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*.model filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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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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license: apache-2.0
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datasets:
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- s2orc
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- flax-sentence-embeddings/stackexchange_xml
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- ms_marco
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- gooaq
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- yahoo_answers_topics
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- code_search_net
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- search_qa
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- eli5
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- snli
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- multi_nli
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- wikihow
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- natural_questions
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- trivia_qa
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- embedding-data/sentence-compression
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- embedding-data/flickr30k-captions
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- embedding-data/altlex
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- embedding-data/simple-wiki
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- embedding-data/QQP
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- embedding-data/SPECTER
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- embedding-data/PAQ_pairs
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- embedding-data/WikiAnswers
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---
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# ONNX version of intfloat/multilingual-e5-small
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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. The ONNX version of this model is made for the [Metarank](https://github.com/metarank/metarank) re-ranker
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to do semantic similarity.
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Check out the [main Metarank docs](https://docs.metarank.ai) on how to configure it.
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TLDR:
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```yaml
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- type: field_match
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name: title_query_match
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rankingField: ranking.query
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itemField: item.title
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distance: cos
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method:
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type: bi-encoder
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model: metarank/multilingual-e5-small
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```
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## License
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Apache 2.0
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config.json
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{
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"_name_or_path": "intfloat/multilingual-e5-small",
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"architectures": [
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"BertModel"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 384,
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"initializer_range": 0.02,
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"intermediate_size": 1536,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"tokenizer_class": "XLMRobertaTokenizer",
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"transformers_version": "4.31.0",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 250037
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}
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pytorch_model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:4aa845c27760e06e9a686b9d8b5d440eae4b6612cd09e5b522b716d3941f77ff
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size 470268533
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sentencepiece.bpe.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865
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size 5069051
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special_tokens_map.json
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{
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"bos_token": "<s>",
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"cls_token": "<s>",
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"eos_token": "</s>",
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"mask_token": "<mask>",
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"pad_token": "<pad>",
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"sep_token": "</s>",
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"unk_token": "<unk>"
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}
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:0b44a9d7b51c3c62626640cda0e2c2f70fdacdc25bbbd68038369d14ebdf4c39
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size 17082730
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tokenizer_config.json
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{
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"bos_token": "<s>",
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"clean_up_tokenization_spaces": true,
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"cls_token": "<s>",
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"eos_token": "</s>",
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"mask_token": {
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"__type": "AddedToken",
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"content": "<mask>",
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"lstrip": true,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"model_max_length": 512,
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"pad_token": "<pad>",
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"sep_token": "</s>",
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"sp_model_kwargs": {},
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"tokenizer_class": "XLMRobertaTokenizer",
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"unk_token": "<unk>"
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}
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