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SenhorDasMoscas
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acho2-ptbr-e4-lr3e-05

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:10822
loss:CosineSimilarityLoss
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
SenhorDasMoscas commited on
Commit
1df8b62
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1 Parent(s): 50d4566

Add new SentenceTransformer model.

Browse files
Files changed (11) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +693 -0
  3. config.json +32 -0
  4. config_sentence_transformers.json +10 -0
  5. model.safetensors +3 -0
  6. modules.json +14 -0
  7. sentence_bert_config.json +4 -0
  8. special_tokens_map.json +37 -0
  9. tokenizer.json +0 -0
  10. tokenizer_config.json +65 -0
  11. vocab.txt +0 -0
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 1024,
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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
@@ -0,0 +1,693 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ tags:
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+ - sentence-transformers
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+ - sentence-similarity
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+ - feature-extraction
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+ - generated_from_trainer
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+ - dataset_size:10822
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+ - loss:CosineSimilarityLoss
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+ base_model: neuralmind/bert-large-portuguese-cased
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+ widget:
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+ - source_sentence: plastificadora documento a4
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+ sentences:
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+ - produto destinar colecionador figura acao selo moeda quadrinho raro item historico
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+ comumente vender loja especializar feira tematico
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+ - caderno lapis caneta mochila escolar item escritorio grampeador post-it alir papel
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+ especial trabalho artistico academico
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+ - console videogame controle headsets cadeira gamer jogo diferentes plataforma pc
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+ playstation xbox
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+ - source_sentence: carrinho supermercado brinquedo
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+ sentences:
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+ - caderno lapis caneta mochila escolar item escritorio grampeador post-it alir papel
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+ especial trabalho artistico academico
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+ - caderno lapis caneta mochila escolar item escritorio grampeador post-it alir papel
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+ especial trabalho artistico academico
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+ - produto voltar publico adulto brinquedo sexual jogo adulto
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+ - source_sentence: kit prato iniciante
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+ sentences:
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+ - paes fresco pizza pre-assadas bolo torta produto artesanal cafe sobremeso
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+ - movel utensilio domestico item decorativo produto limpeza acessorio organizacao
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+ manutencao casa
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+ - violoes teclado microfone pedal efeito suporte acessorio corda afinador voltar
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+ musico iniciante profissional
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+ - source_sentence: capacete seguranca
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+ sentences:
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+ - artigo esportivo bola raquete acessorio academia roupa esportiva equipamento esporte
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+ outdoor escalada ciclismo
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+ - tinta cimento ferramenta construcao material reforma piso azulejo equipamento
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+ protecao individual
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+ - caderno lapis caneta mochila escolar item escritorio grampeador post-it alir papel
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+ especial trabalho artistico academico
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+ - source_sentence: livro ficcao
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+ sentences:
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+ - produto voltar publico adulto brinquedo sexual jogo adulto
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+ - caderno lapis caneta mochila escolar item escritorio grampeador post-it alir papel
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+ especial trabalho artistico academico
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+ - produto basico arroz feijao massa item mercearia snack alimento congelar dia dia
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+ situacoes emergencial
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+ pipeline_tag: sentence-similarity
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+ library_name: sentence-transformers
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+ metrics:
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+ - pearson_cosine
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+ - spearman_cosine
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+ model-index:
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+ - name: SentenceTransformer based on neuralmind/bert-large-portuguese-cased
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+ results:
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+ - task:
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+ type: semantic-similarity
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+ name: Semantic Similarity
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+ dataset:
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+ name: eval similarity
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+ type: eval-similarity
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.9024497617955924
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.8404221831399815
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+ name: Spearman Cosine
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+ ---
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+
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+ # SentenceTransformer based on neuralmind/bert-large-portuguese-cased
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** Sentence Transformer
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+ - **Base model:** [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) <!-- at revision aa302f6ea73b759f7df9cad58bd272127b67ec28 -->
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Output Dimensionality:** 1024 dimensions
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+ - **Similarity Function:** Cosine Similarity
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+ <!-- - **Training Dataset:** Unknown -->
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
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+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
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+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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+
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+ ### Full Model Architecture
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+
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+ ```
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+ SentenceTransformer(
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+ (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
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+ (1): Pooling({'word_embedding_dimension': 1024, '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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+ ## Usage
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+
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+ ### Direct Usage (Sentence Transformers)
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+
106
+ First install the Sentence Transformers library:
107
+
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+ ```bash
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+ pip install -U sentence-transformers
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+ ```
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+
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+ Then you can load this model and run inference.
113
+ ```python
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+ from sentence_transformers import SentenceTransformer
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+
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+ # Download from the 🤗 Hub
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+ model = SentenceTransformer("SenhorDasMoscas/acho2-ptbr-e4-lr3e-05")
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+ # Run inference
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+ sentences = [
120
+ 'livro ficcao',
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+ 'produto basico arroz feijao massa item mercearia snack alimento congelar dia dia situacoes emergencial',
122
+ 'produto voltar publico adulto brinquedo sexual jogo adulto',
123
+ ]
124
+ embeddings = model.encode(sentences)
125
+ print(embeddings.shape)
126
+ # [3, 1024]
127
+
128
+ # Get the similarity scores for the embeddings
129
+ similarities = model.similarity(embeddings, embeddings)
130
+ print(similarities.shape)
131
+ # [3, 3]
132
+ ```
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+
134
+ <!--
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+ ### Direct Usage (Transformers)
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+
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+ <details><summary>Click to see the direct usage in Transformers</summary>
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+
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+ </details>
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+ -->
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+
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+ <!--
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+ ### Downstream Usage (Sentence Transformers)
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+
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+ You can finetune this model on your own dataset.
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+
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+ <details><summary>Click to expand</summary>
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+
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+ </details>
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+ -->
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+
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+ <!--
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+ ### Out-of-Scope Use
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+
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+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+ -->
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+
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+ ## Evaluation
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+
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+ ### Metrics
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+
162
+ #### Semantic Similarity
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+
164
+ * Dataset: `eval-similarity`
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+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
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+
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+ | Metric | Value |
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+ |:--------------------|:-----------|
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+ | pearson_cosine | 0.9024 |
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+ | **spearman_cosine** | **0.8404** |
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+
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+ <!--
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+ ## Bias, Risks and Limitations
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+
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+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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+ -->
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+
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+ <!--
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+ ### Recommendations
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+
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+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
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+ ## Training Details
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+
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+ ### Training Dataset
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+
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+ #### Unnamed Dataset
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+
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+
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+ * Size: 10,822 training samples
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+ * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | text1 | text2 | label |
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+ |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------|
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+ | type | string | string | float |
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+ | details | <ul><li>min: 3 tokens</li><li>mean: 7.16 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 25.08 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 0.1</li><li>mean: 0.53</li><li>max: 1.0</li></ul> |
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+ * Samples:
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+ | text1 | text2 | label |
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+ |:---------------------------------|:----------------------------------------------------------------------------------------------------------------------------|:-----------------|
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+ | <code>tenis nike</code> | <code>artigo esportivo bola raquete acessorio academia roupa esportiva equipamento esporte outdoor escalada ciclismo</code> | <code>1.0</code> |
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+ | <code>tapete Sao Carlos</code> | <code>tinta cimento ferramenta construcao material reforma piso azulejo equipamento protecao individual</code> | <code>0.1</code> |
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+ | <code>kit sensual lua Mel</code> | <code>produto voltar publico adulto brinquedo sexual jogo adulto</code> | <code>1.0</code> |
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+ * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
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+ ```json
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+ {
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+ "loss_fct": "torch.nn.modules.loss.MSELoss"
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+ }
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+ ```
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+
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+ ### Evaluation Dataset
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+
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+ #### Unnamed Dataset
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+
215
+
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+ * Size: 1,203 evaluation samples
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+ * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | text1 | text2 | label |
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+ |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------|
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+ | type | string | string | float |
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+ | details | <ul><li>min: 3 tokens</li><li>mean: 7.09 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 25.62 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 0.1</li><li>mean: 0.57</li><li>max: 1.0</li></ul> |
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+ * Samples:
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+ | text1 | text2 | label |
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+ |:----------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
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+ | <code>carvao</code> | <code>tinta cimento ferramenta construcao material reforma piso azulejo equipamento protecao individual</code> | <code>1.0</code> |
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+ | <code>telha fibrocimento</code> | <code>produto basico arroz feijao massa item mercearia snack alimento congelar dia dia situacoes emergencial</code> | <code>0.1</code> |
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+ | <code>racao cachorro pedigree loja decoracao</code> | <code>baloe paineis decorativo item tematico casamento aniversario luminaria bandeirola vela acessorio transformar ambiente festa ocasioes especial</code> | <code>0.1</code> |
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+ * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
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+ ```json
231
+ {
232
+ "loss_fct": "torch.nn.modules.loss.MSELoss"
233
+ }
234
+ ```
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+
236
+ ### Training Hyperparameters
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+ #### Non-Default Hyperparameters
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+
239
+ - `eval_strategy`: steps
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+ - `per_device_train_batch_size`: 32
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+ - `per_device_eval_batch_size`: 32
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+ - `learning_rate`: 3e-05
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+ - `weight_decay`: 0.1
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+ - `num_train_epochs`: 4
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+ - `warmup_ratio`: 0.1
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+ - `warmup_steps`: 135
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+ - `fp16`: True
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+ - `load_best_model_at_end`: True
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+
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+ #### All Hyperparameters
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+ <details><summary>Click to expand</summary>
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+
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+ - `overwrite_output_dir`: False
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+ - `do_predict`: False
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+ - `eval_strategy`: steps
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+ - `prediction_loss_only`: True
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+ - `per_device_train_batch_size`: 32
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+ - `per_device_eval_batch_size`: 32
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+ - `per_gpu_train_batch_size`: None
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+ - `per_gpu_eval_batch_size`: None
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+ - `gradient_accumulation_steps`: 1
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+ - `eval_accumulation_steps`: None
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+ - `torch_empty_cache_steps`: None
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+ - `learning_rate`: 3e-05
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+ - `weight_decay`: 0.1
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+ - `adam_beta1`: 0.9
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+ - `adam_beta2`: 0.999
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+ - `adam_epsilon`: 1e-08
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+ - `max_grad_norm`: 1.0
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+ - `num_train_epochs`: 4
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+ - `max_steps`: -1
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+ - `lr_scheduler_type`: linear
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+ - `lr_scheduler_kwargs`: {}
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+ - `warmup_ratio`: 0.1
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+ - `warmup_steps`: 135
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+ - `log_level`: passive
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+ - `log_level_replica`: warning
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+ - `log_on_each_node`: True
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+ - `logging_nan_inf_filter`: True
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+ - `save_safetensors`: True
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+ - `save_on_each_node`: False
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+ - `save_only_model`: False
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+ - `restore_callback_states_from_checkpoint`: False
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+ - `no_cuda`: False
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+ - `use_cpu`: False
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+ - `use_mps_device`: False
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+ - `seed`: 42
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+ - `data_seed`: None
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+ - `jit_mode_eval`: False
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+ - `use_ipex`: False
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+ - `bf16`: False
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+ - `fp16`: True
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+ - `fp16_opt_level`: O1
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+ - `half_precision_backend`: auto
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+ - `bf16_full_eval`: False
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+ - `fp16_full_eval`: False
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+ - `tf32`: None
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+ - `local_rank`: 0
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+ - `ddp_backend`: None
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+ - `tpu_num_cores`: None
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+ - `tpu_metrics_debug`: False
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+ - `debug`: []
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+ - `dataloader_drop_last`: False
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+ - `dataloader_num_workers`: 0
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+ - `dataloader_prefetch_factor`: None
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+ - `past_index`: -1
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+ - `disable_tqdm`: False
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+ - `remove_unused_columns`: True
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+ - `label_names`: None
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+ - `load_best_model_at_end`: True
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+ - `ignore_data_skip`: False
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+ - `fsdp`: []
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+ - `fsdp_min_num_params`: 0
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+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
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+ - `fsdp_transformer_layer_cls_to_wrap`: None
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+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
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+ - `deepspeed`: None
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+ - `label_smoothing_factor`: 0.0
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+ - `optim`: adamw_torch
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+ - `optim_args`: None
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+ - `adafactor`: False
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+ - `group_by_length`: False
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+ - `length_column_name`: length
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+ - `ddp_find_unused_parameters`: None
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+ - `ddp_bucket_cap_mb`: None
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+ - `ddp_broadcast_buffers`: False
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+ - `dataloader_pin_memory`: True
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+ - `dataloader_persistent_workers`: False
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+ - `skip_memory_metrics`: True
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+ - `use_legacy_prediction_loop`: False
331
+ - `push_to_hub`: False
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+ - `resume_from_checkpoint`: None
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+ - `hub_model_id`: None
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+ - `hub_strategy`: every_save
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+ - `hub_private_repo`: None
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+ - `hub_always_push`: False
337
+ - `gradient_checkpointing`: False
338
+ - `gradient_checkpointing_kwargs`: None
339
+ - `include_inputs_for_metrics`: False
340
+ - `include_for_metrics`: []
341
+ - `eval_do_concat_batches`: True
342
+ - `fp16_backend`: auto
343
+ - `push_to_hub_model_id`: None
344
+ - `push_to_hub_organization`: None
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+ - `mp_parameters`:
346
+ - `auto_find_batch_size`: False
347
+ - `full_determinism`: False
348
+ - `torchdynamo`: None
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+ - `ray_scope`: last
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+ - `ddp_timeout`: 1800
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+ - `torch_compile`: False
352
+ - `torch_compile_backend`: None
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+ - `torch_compile_mode`: None
354
+ - `dispatch_batches`: None
355
+ - `split_batches`: None
356
+ - `include_tokens_per_second`: False
357
+ - `include_num_input_tokens_seen`: False
358
+ - `neftune_noise_alpha`: None
359
+ - `optim_target_modules`: None
360
+ - `batch_eval_metrics`: False
361
+ - `eval_on_start`: False
362
+ - `use_liger_kernel`: False
363
+ - `eval_use_gather_object`: False
364
+ - `average_tokens_across_devices`: False
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+ - `prompts`: None
366
+ - `batch_sampler`: batch_sampler
367
+ - `multi_dataset_batch_sampler`: proportional
368
+
369
+ </details>
370
+
371
+ ### Training Logs
372
+ <details><summary>Click to expand</summary>
373
+
374
+ | Epoch | Step | Training Loss | Validation Loss | eval-similarity_spearman_cosine |
375
+ |:----------:|:--------:|:-------------:|:---------------:|:-------------------------------:|
376
+ | 0.0147 | 5 | 0.2323 | - | - |
377
+ | 0.0295 | 10 | 0.2056 | - | - |
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+ | 0.0442 | 15 | 0.2203 | - | - |
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+ | 0.0590 | 20 | 0.1947 | - | - |
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+ | 0.0737 | 25 | 0.1811 | - | - |
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+ | 0.0885 | 30 | 0.1526 | - | - |
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+ | 0.1032 | 35 | 0.1511 | - | - |
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+ | 0.1180 | 40 | 0.1543 | - | - |
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+ | 0.1327 | 45 | 0.1529 | - | - |
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+ | 0.1475 | 50 | 0.1296 | - | - |
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+ | 0.1622 | 55 | 0.1212 | - | - |
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+ | 0.1770 | 60 | 0.1023 | - | - |
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+ | 0.1917 | 65 | 0.1011 | - | - |
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+ | 0.2065 | 70 | 0.1047 | - | - |
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+ | 0.2212 | 75 | 0.1077 | - | - |
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+ | 0.2360 | 80 | 0.0909 | - | - |
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+ | 0.2507 | 85 | 0.0913 | - | - |
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+ | 0.2655 | 90 | 0.1045 | - | - |
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+ | 0.2802 | 95 | 0.0761 | - | - |
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+ | 0.2950 | 100 | 0.0705 | - | - |
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+ | 0.3097 | 105 | 0.086 | - | - |
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+ | 0.3245 | 110 | 0.0753 | - | - |
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+ | 0.3392 | 115 | 0.0652 | - | - |
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+ | 0.3540 | 120 | 0.0663 | - | - |
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+ | 0.3687 | 125 | 0.0862 | - | - |
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+ | 0.3835 | 130 | 0.085 | - | - |
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+ | 0.3982 | 135 | 0.0803 | - | - |
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+ | 0.4130 | 140 | 0.088 | - | - |
404
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492
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493
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494
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495
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496
+ | 1.7847 | 605 | 0.0482 | - | - |
497
+ | 1.7994 | 610 | 0.0546 | - | - |
498
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499
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501
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506
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513
+ | 2.0354 | 690 | 0.0299 | - | - |
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647
+
648
+ * The bold row denotes the saved checkpoint.
649
+ </details>
650
+
651
+ ### Framework Versions
652
+ - Python: 3.10.12
653
+ - Sentence Transformers: 3.3.1
654
+ - Transformers: 4.47.1
655
+ - PyTorch: 2.5.1+cu121
656
+ - Accelerate: 1.1.1
657
+ - Datasets: 2.14.4
658
+ - Tokenizers: 0.21.0
659
+
660
+ ## Citation
661
+
662
+ ### BibTeX
663
+
664
+ #### Sentence Transformers
665
+ ```bibtex
666
+ @inproceedings{reimers-2019-sentence-bert,
667
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
668
+ author = "Reimers, Nils and Gurevych, Iryna",
669
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
670
+ month = "11",
671
+ year = "2019",
672
+ publisher = "Association for Computational Linguistics",
673
+ url = "https://arxiv.org/abs/1908.10084",
674
+ }
675
+ ```
676
+
677
+ <!--
678
+ ## Glossary
679
+
680
+ *Clearly define terms in order to be accessible across audiences.*
681
+ -->
682
+
683
+ <!--
684
+ ## Model Card Authors
685
+
686
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
687
+ -->
688
+
689
+ <!--
690
+ ## Model Card Contact
691
+
692
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
693
+ -->
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+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "101": {
20
+ "content": "[CLS]",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "102": {
28
+ "content": "[SEP]",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "103": {
36
+ "content": "[MASK]",
37
+ "lstrip": false,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ }
43
+ },
44
+ "clean_up_tokenization_spaces": true,
45
+ "cls_token": "[CLS]",
46
+ "do_basic_tokenize": true,
47
+ "do_lower_case": false,
48
+ "extra_special_tokens": {},
49
+ "mask_token": "[MASK]",
50
+ "max_length": 512,
51
+ "model_max_length": 512,
52
+ "never_split": null,
53
+ "pad_to_multiple_of": null,
54
+ "pad_token": "[PAD]",
55
+ "pad_token_type_id": 0,
56
+ "padding_side": "right",
57
+ "sep_token": "[SEP]",
58
+ "stride": 0,
59
+ "strip_accents": null,
60
+ "tokenize_chinese_chars": true,
61
+ "tokenizer_class": "BertTokenizer",
62
+ "truncation_side": "right",
63
+ "truncation_strategy": "longest_first",
64
+ "unk_token": "[UNK]"
65
+ }
vocab.txt ADDED
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