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  1. README.md +61 -0
  2. config.json +26 -0
  3. model.onnx +3 -0
  4. special_tokens_map.json +37 -0
  5. tokenizer.json +0 -0
  6. tokenizer_config.json +64 -0
  7. vocab.txt +0 -0
README.md ADDED
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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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+ ---
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+
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+ # onnx-models/all-MiniLM-L6-v2-fine-tuned-sent-label-epochs-10-iter-10-batch-32-onnx
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+
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+ This is the ONNX-ported version of the [event-nlp/all-MiniLM-L6-v2-fine-tuned-sent-label-epochs-10-iter-10-batch-32](https://huggingface.co/event-nlp/all-MiniLM-L6-v2-fine-tuned-sent-label-epochs-10-iter-10-batch-32) for generating text embeddings.
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+
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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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+
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+ <!--- Describe your model here -->
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+
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+ ## Usage
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+
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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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+ ```
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+ pip install -U light-embed
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+ ```
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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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+
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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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+
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+ model = TextEmbedding('event-nlp/all-MiniLM-L6-v2-fine-tuned-sent-label-epochs-10-iter-10-batch-32')
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+ embeddings = model.encode(sentences)
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+ print(embeddings)
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+ ```
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+
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+ or by specifying the *onnx model name* like this:
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+
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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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+
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+ model = TextEmbedding('onnx-models/all-MiniLM-L6-v2-fine-tuned-sent-label-epochs-10-iter-10-batch-32-onnx')
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+ embeddings = model.encode(sentences)
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+ print(embeddings)
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+ ```
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+
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+ ## Citing & Authors
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+
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+ Binh Nguyen / [email protected]
config.json ADDED
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+ {
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+ "_name_or_path": "event-nlp/all-MiniLM-L6-v2-fine-tuned-sent-label-epochs-10-iter-10-batch-32",
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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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+ }
model.onnx ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:5c7f5a7b08d8a9c434ccadc260bad7e2c50fcf288781d10cac356e92c2e8ae78
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+ size 90445823
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tokenizer.json ADDED
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tokenizer_config.json ADDED
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vocab.txt ADDED
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