binhcode25
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Commit
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Parent(s):
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Add new SentenceTransformer model.
Browse files- README.md +63 -0
- config.json +24 -0
- config_sentence_transformers.json +7 -0
- model.onnx +3 -0
- special_tokens_map.json +37 -0
- tokenizer.json +0 -0
- tokenizer_config.json +64 -0
- vocab.txt +0 -0
README.md
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---
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library_name: light-embed
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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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---
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# LightEmbed/all-MiniLM-L6-v2-onnx
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This is the ONNX version of the Sentence Transformers model sentence-transformers/all-MiniLM-L6-v2 (https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) for sentence embedding, optimized for speed and lightweight performance. By utilizing onnxruntime and tokenizers instead of heavier libraries like sentence-transformers and transformers, this version ensures a smaller library size and faster execution. Below are the details of the model:
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- Base model: sentence-transformers/all-MiniLM-L6-v2
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- Embedding dimension: 384
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- Max sequence length: 256
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- File size on disk: 0.08 GB
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- Pooling incorporated: Yes
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This ONNX model consists all components in the original sentence transformer model:
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Transformer, Pooling, Normalize
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<!--- Describe your model here -->
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## Usage (LightEmbed)
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Using this model becomes easy when you have [LightEmbed](https://pypi.org/project/light-embed/) installed:
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```
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pip install -U light-embed
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```
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Then you can use the model using the original model name like this:
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```python
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from light_embed import TextEmbedding
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sentences = [
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"This is an example sentence",
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"Each sentence is converted"
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]
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model = TextEmbedding('sentence-transformers/all-MiniLM-L6-v2')
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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Then you can use the model using onnx model name like this:
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```python
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from light_embed import TextEmbedding
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sentences = [
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"This is an example sentence",
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"Each sentence is converted"
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]
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model = TextEmbedding('LightEmbed/all-MiniLM-L6-v2-onnx')
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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## Citing & Authors
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Binh Nguyen / [email protected]
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config.json
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{
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"_name_or_path": "sentence-transformers/all-MiniLM-L6-v2",
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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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"gradient_checkpointing": false,
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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": 6,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"transformers_version": "4.8.2",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30522
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}
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config_sentence_transformers.json
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{
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"__version__": {
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"sentence_transformers": "2.0.0",
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"transformers": "4.6.1",
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"pytorch": "1.8.1"
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}
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}
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model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:3cc4e13264bea58a3257cec9821637bcab95e687137a07152e08ab3af37f809f
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size 90446240
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special_tokens_map.json
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{
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"cls_token": {
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"content": "[CLS]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"mask_token": {
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"content": "[MASK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "[PAD]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"sep_token": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.json
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tokenizer_config.json
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{
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"added_tokens_decoder": {
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"0": {
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"content": "[PAD]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"100": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"101": {
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"content": "[CLS]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"102": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"103": {
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"content": "[MASK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"clean_up_tokenization_spaces": true,
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"cls_token": "[CLS]",
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"do_basic_tokenize": true,
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"do_lower_case": true,
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"mask_token": "[MASK]",
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"max_length": 128,
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"model_max_length": 256,
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"never_split": null,
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"pad_to_multiple_of": null,
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"pad_token": "[PAD]",
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"pad_token_type_id": 0,
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"padding_side": "right",
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"sep_token": "[SEP]",
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"stride": 0,
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"truncation_side": "right",
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"truncation_strategy": "longest_first",
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"unk_token": "[UNK]"
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}
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vocab.txt
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