lambdaofgod commited on
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Add new SentenceTransformer model.

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0_WordEmbeddings/whitespacetokenizer_config.json ADDED
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0_WordEmbeddings/wordembedding_config.json ADDED
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+ {
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+ "tokenizer_class": "mlutil.sentence_transformers_utils.CustomTokenizer",
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+ "update_embeddings": false,
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+ "max_seq_length": 1000000
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+ }
1_WordWeights/config.json ADDED
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2_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 200,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false
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+ }
README.md ADDED
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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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+
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+ ---
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+
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+ # lambdaofgod/document-titles_dependencies-nbow-nbow-mnrl
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 200 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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+
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+ <!--- Describe your model here -->
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+
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+ ## Usage (Sentence-Transformers)
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+
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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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+ ```
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+ pip install -U sentence-transformers
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+ ```
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+
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+ Then you can use the model like this:
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+
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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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+
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+ model = SentenceTransformer('lambdaofgod/document-titles_dependencies-nbow-nbow-mnrl')
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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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+
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+
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+ ## Evaluation Results
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+
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+ <!--- Describe how your model was evaluated -->
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+
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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=lambdaofgod/document-titles_dependencies-nbow-nbow-mnrl)
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+
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+
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+
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+ ## Full Model Architecture
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+ ```
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+ SentenceTransformer(
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+ (0): WordEmbeddings(
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+ (emb_layer): Embedding(53559, 200)
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+ )
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+ (1): WordWeights(
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+ (emb_layer): Embedding(53559, 1)
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+ )
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+ (2): Pooling({'word_embedding_dimension': 200, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
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+ )
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+ ```
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+
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+ ## Citing & Authors
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+
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+ <!--- Describe where people can find more information -->
config_sentence_transformers.json ADDED
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+ {
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+ "__version__": {
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+ "sentence_transformers": "2.2.2",
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+ "transformers": "4.20.0",
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+ "pytorch": "1.10.0+cu111"
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+ }
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+ }
modules.json ADDED
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+ [
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+ {
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+ "idx": 0,
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+ "name": "0",
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+ "path": "0_WordEmbeddings",
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+ "type": "sentence_transformers.models.WordEmbeddings"
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+ },
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+ {
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+ "idx": 1,
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+ "name": "1",
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+ "path": "1_WordWeights",
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+ "type": "sentence_transformers.models.WordWeights"
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+ },
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+ {
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+ "idx": 2,
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+ "name": "2",
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+ "path": "2_Pooling",
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+ "type": "sentence_transformers.models.Pooling"
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+ }
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+ ]