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Add new SentenceTransformer model.

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0_WordEmbeddings/wordembedding_config.json ADDED
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+ {
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+ "tokenizer_class": "sentence_transformers.models.tokenizer.WhitespaceTokenizer.WhitespaceTokenizer",
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+ "update_embeddings": false,
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+ "max_seq_length": 1000000
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+ }
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 1000,
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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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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
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+ ---
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+ library_name: sentence-transformers
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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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+ language:
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+ - pt
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+ ---
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+
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+ # mteb-pt/average_pt_nilc_word2vec_skip_s1000
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+
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+ This is an adaptation of pre-trained Portuguese Word2Vec Word Embeddings to a [sentence-transformers](https://www.SBERT.net) model.
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+
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+ The original pre-trained word embeddings can be found at: [http://nilc.icmc.usp.br/nilc/index.php/repositorio-de-word-embeddings-do-nilc](http://nilc.icmc.usp.br/nilc/index.php/repositorio-de-word-embeddings-do-nilc).
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+
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+ This model maps sentences & paragraphs to a 1000 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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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('mteb-pt/average_pt_nilc_word2vec_skip_s1000')
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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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+ ## Evaluation Results
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+
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+ For an automated evaluation of this model, see the *Portuguese MTEB Leaderboard*: [mteb-pt/leaderboard](https://huggingface.co/spaces/mteb-pt/leaderboard)
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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(929607, 1000)
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+ )
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+ (1): Pooling({'word_embedding_dimension': 1000, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
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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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+ ```bibtex
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+ @inproceedings{hartmann2017portuguese,
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+ title = {Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks},
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+ author = {Hartmann, Nathan S and
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+ Fonseca, Erick R and
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+ Shulby, Christopher D and
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+ Treviso, Marcos V and
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+ Rodrigues, J{'{e}}ssica S and
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+ Alu{'{\i}}sio, Sandra Maria},
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+ year = {2017},
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+ publisher = {SBC},
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+ booktitle = {Brazilian Symposium in Information and Human Language Technology - STIL},
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+ url = {https://sol.sbc.org.br/index.php/stil/article/view/4008}
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+ }
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+ ```
config_sentence_transformers.json ADDED
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+ {
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+ "__version__": {
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+ "sentence_transformers": "2.6.1",
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+ "transformers": "4.39.0.dev0",
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+ "pytorch": "2.2.2"
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+ },
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+ "prompts": {},
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+ "default_prompt_name": null
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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_Pooling",
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+ "type": "sentence_transformers.models.Pooling"
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+ }
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+ ]