binhcode25
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Browse files- README.md +61 -0
- config.json +26 -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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# onnx-models/all-MiniLM-L6-v2-fine-tuned-epochs-50-iter-20-onnx
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This is the ONNX-ported version of the [event-nlp/all-MiniLM-L6-v2-fine-tuned-epochs-50-iter-20](https://huggingface.co/event-nlp/all-MiniLM-L6-v2-fine-tuned-epochs-50-iter-20) for generating text embeddings.
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## Model details
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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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- Modules incorporated in the onnx: Transformer, Pooling, Normalize
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<!--- Describe your model here -->
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## Usage
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Using this model becomes easy when you have [light-embed](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 by specifying 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('event-nlp/all-MiniLM-L6-v2-fine-tuned-epochs-50-iter-20')
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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or by specifying the *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('onnx-models/all-MiniLM-L6-v2-fine-tuned-epochs-50-iter-20-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": "event-nlp/all-MiniLM-L6-v2-fine-tuned-epochs-50-iter-20",
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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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"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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"torch_dtype": "float32",
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"transformers_version": "4.30.2",
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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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model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:278cafcb6e8d60fd80ab500605d0217cf2bffbd49b0b427255fafb1f39d1da2c
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size 90445823
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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": 256,
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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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