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SenhorDasMoscas
/
bert-ptbr-e3-lr0.0001-04-01-2025

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:18623
loss:CosineSimilarityLoss
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
SenhorDasMoscas commited on
Commit
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verified ·
1 Parent(s): 3cbbd54

Add new SentenceTransformer model.

Browse files
Files changed (11) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +758 -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
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
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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 +1,758 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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:18623
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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: maquina cafe expresso cadence
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+ sentences:
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+ - beleza autocuidado
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+ - eletrodomestico
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+ - produto alimenticio basico
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+ - source_sentence: alicate corte
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+ sentences:
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+ - esporte fitness
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+ - brinquedo jogo educativo
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+ - item adulto brinquedo sexual
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+ - source_sentence: luminaria neon
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+ sentences:
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+ - casa decoracao
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+ - brinquedo jogo educativo
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+ - produto pet animal domestico
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+ - source_sentence: sofa 3 lugar
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+ sentences:
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+ - produto pet animal domestico
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+ - casa decoracao
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+ - papelaria escritorio
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+ - source_sentence: cobertor pelucia
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+ sentences:
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+ - joia bijuterio
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+ - servico reparo eletronico
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+ - moda acessorio
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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: 0.9058132977545096
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.8399056573091899
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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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+
92
+ ### Direct Usage (Sentence Transformers)
93
+
94
+ First install the Sentence Transformers library:
95
+
96
+ ```bash
97
+ pip install -U sentence-transformers
98
+ ```
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+
100
+ Then you can load this model and run inference.
101
+ ```python
102
+ from sentence_transformers import SentenceTransformer
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+
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+ # Download from the 🤗 Hub
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+ model = SentenceTransformer("SenhorDasMoscas/bert-ptbr-e3-lr0.0001-04-01-2025")
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+ # Run inference
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+ sentences = [
108
+ 'cobertor pelucia',
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+ 'moda acessorio',
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+ 'servico reparo eletronico',
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+ ]
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+ embeddings = model.encode(sentences)
113
+ print(embeddings.shape)
114
+ # [3, 1024]
115
+
116
+ # Get the similarity scores for the embeddings
117
+ similarities = model.similarity(embeddings, embeddings)
118
+ print(similarities.shape)
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+ # [3, 3]
120
+ ```
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+
122
+ <!--
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+ ### Direct Usage (Transformers)
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+
125
+ <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>
136
+
137
+ </details>
138
+ -->
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+
140
+ <!--
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+ ### Out-of-Scope Use
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+
143
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
144
+ -->
145
+
146
+ ## Evaluation
147
+
148
+ ### Metrics
149
+
150
+ #### Semantic Similarity
151
+
152
+ * Dataset: `eval-similarity`
153
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
154
+
155
+ | Metric | Value |
156
+ |:--------------------|:-----------|
157
+ | pearson_cosine | 0.9058 |
158
+ | **spearman_cosine** | **0.8399** |
159
+
160
+ <!--
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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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+
166
+ <!--
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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: 18,623 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 |
183
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
184
+ | type | string | string | float |
185
+ | details | <ul><li>min: 3 tokens</li><li>mean: 7.67 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.58 tokens</li><li>max: 11 tokens</li></ul> | <ul><li>min: 0.1</li><li>mean: 0.54</li><li>max: 1.0</li></ul> |
186
+ * Samples:
187
+ | text1 | text2 | label |
188
+ |:--------------------------------------------|:--------------------------------------|:-----------------|
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+ | <code>tabua carne</code> | <code>casa decoracao</code> | <code>1.0</code> |
190
+ | <code>caminhaor basculante brinquedo</code> | <code>brinquedo jogo educativo</code> | <code>1.0</code> |
191
+ | <code>buscar mochila escolar crianca</code> | <code>comida rapido fastfood</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:
193
+ ```json
194
+ {
195
+ "loss_fct": "torch.nn.modules.loss.MSELoss"
196
+ }
197
+ ```
198
+
199
+ ### Evaluation Dataset
200
+
201
+ #### Unnamed Dataset
202
+
203
+
204
+ * Size: 2,070 evaluation samples
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+ * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
206
+ * Approximate statistics based on the first 1000 samples:
207
+ | | text1 | text2 | label |
208
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
209
+ | type | string | string | float |
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+ | details | <ul><li>min: 3 tokens</li><li>mean: 7.69 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.54 tokens</li><li>max: 11 tokens</li></ul> | <ul><li>min: 0.1</li><li>mean: 0.59</li><li>max: 1.0</li></ul> |
211
+ * Samples:
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+ | text1 | text2 | label |
213
+ |:------------------------------------------|:--------------------------------------|:-----------------|
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+ | <code>preciso pao frances integral</code> | <code>padaria confeitaria</code> | <code>1.0</code> |
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+ | <code>onde poder comprar microfone</code> | <code>joia bijuterio</code> | <code>0.1</code> |
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+ | <code>chuveiro eletrico lorenzetti</code> | <code>livro material literario</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:
218
+ ```json
219
+ {
220
+ "loss_fct": "torch.nn.modules.loss.MSELoss"
221
+ }
222
+ ```
223
+
224
+ ### Training Hyperparameters
225
+ #### Non-Default Hyperparameters
226
+
227
+ - `eval_strategy`: steps
228
+ - `per_device_train_batch_size`: 32
229
+ - `per_device_eval_batch_size`: 32
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+ - `learning_rate`: 0.0001
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+ - `weight_decay`: 0.1
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+ - `warmup_ratio`: 0.1
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+ - `warmup_steps`: 232
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+ - `fp16`: True
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+ - `load_best_model_at_end`: True
236
+
237
+ #### All Hyperparameters
238
+ <details><summary>Click to expand</summary>
239
+
240
+ - `overwrite_output_dir`: False
241
+ - `do_predict`: False
242
+ - `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`: 0.0001
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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`: 3
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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`: 232
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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
281
+ - `half_precision_backend`: auto
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+ - `bf16_full_eval`: False
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+ - `fp16_full_eval`: False
284
+ - `tf32`: None
285
+ - `local_rank`: 0
286
+ - `ddp_backend`: None
287
+ - `tpu_num_cores`: None
288
+ - `tpu_metrics_debug`: False
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+ - `debug`: []
290
+ - `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
297
+ - `load_best_model_at_end`: True
298
+ - `ignore_data_skip`: False
299
+ - `fsdp`: []
300
+ - `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}
302
+ - `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
314
+ - `dataloader_pin_memory`: True
315
+ - `dataloader_persistent_workers`: False
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+ - `skip_memory_metrics`: True
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+ - `use_legacy_prediction_loop`: False
318
+ - `push_to_hub`: False
319
+ - `resume_from_checkpoint`: None
320
+ - `hub_model_id`: None
321
+ - `hub_strategy`: every_save
322
+ - `hub_private_repo`: None
323
+ - `hub_always_push`: False
324
+ - `gradient_checkpointing`: False
325
+ - `gradient_checkpointing_kwargs`: None
326
+ - `include_inputs_for_metrics`: False
327
+ - `include_for_metrics`: []
328
+ - `eval_do_concat_batches`: True
329
+ - `fp16_backend`: auto
330
+ - `push_to_hub_model_id`: None
331
+ - `push_to_hub_organization`: None
332
+ - `mp_parameters`:
333
+ - `auto_find_batch_size`: False
334
+ - `full_determinism`: False
335
+ - `torchdynamo`: None
336
+ - `ray_scope`: last
337
+ - `ddp_timeout`: 1800
338
+ - `torch_compile`: False
339
+ - `torch_compile_backend`: None
340
+ - `torch_compile_mode`: None
341
+ - `dispatch_batches`: None
342
+ - `split_batches`: None
343
+ - `include_tokens_per_second`: False
344
+ - `include_num_input_tokens_seen`: False
345
+ - `neftune_noise_alpha`: None
346
+ - `optim_target_modules`: None
347
+ - `batch_eval_metrics`: False
348
+ - `eval_on_start`: False
349
+ - `use_liger_kernel`: False
350
+ - `eval_use_gather_object`: False
351
+ - `average_tokens_across_devices`: False
352
+ - `prompts`: None
353
+ - `batch_sampler`: batch_sampler
354
+ - `multi_dataset_batch_sampler`: proportional
355
+
356
+ </details>
357
+
358
+ ### Training Logs
359
+ <details><summary>Click to expand</summary>
360
+
361
+ | Epoch | Step | Training Loss | Validation Loss | eval-similarity_spearman_cosine |
362
+ |:----------:|:--------:|:-------------:|:---------------:|:-------------------------------:|
363
+ | 0.0086 | 5 | 0.2031 | - | - |
364
+ | 0.0172 | 10 | 0.2078 | - | - |
365
+ | 0.0258 | 15 | 0.2062 | - | - |
366
+ | 0.0344 | 20 | 0.1693 | - | - |
367
+ | 0.0430 | 25 | 0.1681 | - | - |
368
+ | 0.0515 | 30 | 0.1639 | - | - |
369
+ | 0.0601 | 35 | 0.1393 | - | - |
370
+ | 0.0687 | 40 | 0.1675 | - | - |
371
+ | 0.0773 | 45 | 0.1297 | - | - |
372
+ | 0.0859 | 50 | 0.1223 | - | - |
373
+ | 0.0945 | 55 | 0.1203 | - | - |
374
+ | 0.1031 | 60 | 0.0942 | - | - |
375
+ | 0.1117 | 65 | 0.0922 | - | - |
376
+ | 0.1203 | 70 | 0.097 | - | - |
377
+ | 0.1289 | 75 | 0.0927 | - | - |
378
+ | 0.1375 | 80 | 0.0961 | - | - |
379
+ | 0.1460 | 85 | 0.0821 | - | - |
380
+ | 0.1546 | 90 | 0.0621 | - | - |
381
+ | 0.1632 | 95 | 0.084 | - | - |
382
+ | 0.1718 | 100 | 0.0706 | - | - |
383
+ | 0.1804 | 105 | 0.0701 | - | - |
384
+ | 0.1890 | 110 | 0.0828 | - | - |
385
+ | 0.1976 | 115 | 0.078 | - | - |
386
+ | 0.2062 | 120 | 0.0745 | - | - |
387
+ | 0.2148 | 125 | 0.0744 | - | - |
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+ | 0.2234 | 130 | 0.0785 | - | - |
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+ | 0.2320 | 135 | 0.0745 | - | - |
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+ | 0.2405 | 140 | 0.0615 | - | - |
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+ | 0.2491 | 145 | 0.0665 | - | - |
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+ | 0.2577 | 150 | 0.0873 | - | - |
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+ | 0.2663 | 155 | 0.0916 | - | - |
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+ | 0.2749 | 160 | 0.0659 | - | - |
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+ | 0.2835 | 165 | 0.0896 | - | - |
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+ | 0.2921 | 170 | 0.0807 | - | - |
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+ | 0.3007 | 175 | 0.0745 | - | - |
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+ | 0.3093 | 180 | 0.0794 | - | - |
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+ | 0.3179 | 185 | 0.0703 | - | - |
400
+ | 0.3265 | 190 | 0.0705 | - | - |
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+ | 0.3351 | 195 | 0.084 | - | - |
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+ | 0.3436 | 200 | 0.0671 | - | - |
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+ | 0.3522 | 205 | 0.076 | - | - |
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+ | 0.3608 | 210 | 0.0821 | - | - |
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+ | 0.3694 | 215 | 0.0499 | - | - |
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+ | 0.3780 | 220 | 0.0729 | - | - |
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+ | 0.3866 | 225 | 0.0697 | - | - |
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+ | 0.3952 | 230 | 0.085 | - | - |
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+ | 0.4038 | 235 | 0.0835 | - | - |
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+ | 0.4124 | 240 | 0.0743 | - | - |
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+ | 0.4210 | 245 | 0.0714 | - | - |
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+ | 0.4296 | 250 | 0.0597 | - | - |
413
+ | 0.4381 | 255 | 0.0626 | - | - |
414
+ | 0.4467 | 260 | 0.0522 | - | - |
415
+ | 0.4553 | 265 | 0.0734 | - | - |
416
+ | 0.4639 | 270 | 0.0616 | - | - |
417
+ | 0.4725 | 275 | 0.0463 | - | - |
418
+ | 0.4811 | 280 | 0.0631 | - | - |
419
+ | 0.4897 | 285 | 0.0672 | - | - |
420
+ | 0.4983 | 290 | 0.0725 | - | - |
421
+ | 0.5069 | 295 | 0.043 | - | - |
422
+ | 0.5155 | 300 | 0.0675 | 0.0698 | 0.7861 |
423
+ | 0.5241 | 305 | 0.0837 | - | - |
424
+ | 0.5326 | 310 | 0.0785 | - | - |
425
+ | 0.5412 | 315 | 0.0761 | - | - |
426
+ | 0.5498 | 320 | 0.0523 | - | - |
427
+ | 0.5584 | 325 | 0.0514 | - | - |
428
+ | 0.5670 | 330 | 0.0726 | - | - |
429
+ | 0.5756 | 335 | 0.0584 | - | - |
430
+ | 0.5842 | 340 | 0.0736 | - | - |
431
+ | 0.5928 | 345 | 0.0705 | - | - |
432
+ | 0.6014 | 350 | 0.0682 | - | - |
433
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434
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435
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436
+ | 0.6357 | 370 | 0.0657 | - | - |
437
+ | 0.6443 | 375 | 0.0766 | - | - |
438
+ | 0.6529 | 380 | 0.0759 | - | - |
439
+ | 0.6615 | 385 | 0.071 | - | - |
440
+ | 0.6701 | 390 | 0.055 | - | - |
441
+ | 0.6787 | 395 | 0.0466 | - | - |
442
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443
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447
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448
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449
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450
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456
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459
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461
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462
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463
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465
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466
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468
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470
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471
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472
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473
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474
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475
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476
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477
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478
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479
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480
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481
+ | 1.0223 | 595 | 0.0405 | - | - |
482
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483
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484
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485
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486
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487
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488
+ | 1.0825 | 630 | 0.025 | - | - |
489
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490
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491
+ | 1.1082 | 645 | 0.0294 | - | - |
492
+ | 1.1168 | 650 | 0.035 | - | - |
493
+ | 1.1254 | 655 | 0.0376 | - | - |
494
+ | 1.1340 | 660 | 0.0414 | - | - |
495
+ | 1.1426 | 665 | 0.0502 | - | - |
496
+ | 1.1512 | 670 | 0.04 | - | - |
497
+ | 1.1598 | 675 | 0.0385 | - | - |
498
+ | 1.1684 | 680 | 0.0286 | - | - |
499
+ | 1.1770 | 685 | 0.0361 | - | - |
500
+ | 1.1856 | 690 | 0.0282 | - | - |
501
+ | 1.1942 | 695 | 0.0473 | - | - |
502
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503
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504
+ | 1.2199 | 710 | 0.0283 | - | - |
505
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506
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507
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508
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509
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510
+ | 1.2715 | 740 | 0.0398 | - | - |
511
+ | 1.2801 | 745 | 0.0477 | - | - |
512
+ | 1.2887 | 750 | 0.0418 | - | - |
513
+ | 1.2973 | 755 | 0.034 | - | - |
514
+ | 1.3058 | 760 | 0.0397 | - | - |
515
+ | 1.3144 | 765 | 0.0308 | - | - |
516
+ | 1.3230 | 770 | 0.0457 | - | - |
517
+ | 1.3316 | 775 | 0.0328 | - | - |
518
+ | 1.3402 | 780 | 0.0222 | - | - |
519
+ | 1.3488 | 785 | 0.0246 | - | - |
520
+ | 1.3574 | 790 | 0.0229 | - | - |
521
+ | 1.3660 | 795 | 0.0351 | - | - |
522
+ | 1.3746 | 800 | 0.0415 | - | - |
523
+ | 1.3832 | 805 | 0.0351 | - | - |
524
+ | 1.3918 | 810 | 0.0269 | - | - |
525
+ | 1.4003 | 815 | 0.0307 | - | - |
526
+ | 1.4089 | 820 | 0.0381 | - | - |
527
+ | 1.4175 | 825 | 0.0425 | - | - |
528
+ | 1.4261 | 830 | 0.0557 | - | - |
529
+ | 1.4347 | 835 | 0.0523 | - | - |
530
+ | 1.4433 | 840 | 0.0488 | - | - |
531
+ | 1.4519 | 845 | 0.0355 | - | - |
532
+ | 1.4605 | 850 | 0.0403 | - | - |
533
+ | 1.4691 | 855 | 0.0332 | - | - |
534
+ | 1.4777 | 860 | 0.0427 | - | - |
535
+ | 1.4863 | 865 | 0.0348 | - | - |
536
+ | 1.4948 | 870 | 0.0375 | - | - |
537
+ | 1.5034 | 875 | 0.0271 | - | - |
538
+ | 1.5120 | 880 | 0.0428 | - | - |
539
+ | 1.5206 | 885 | 0.0666 | - | - |
540
+ | 1.5292 | 890 | 0.0491 | - | - |
541
+ | 1.5378 | 895 | 0.0424 | - | - |
542
+ | 1.5464 | 900 | 0.0413 | 0.0418 | 0.8326 |
543
+ | 1.5550 | 905 | 0.0469 | - | - |
544
+ | 1.5636 | 910 | 0.0288 | - | - |
545
+ | 1.5722 | 915 | 0.0541 | - | - |
546
+ | 1.5808 | 920 | 0.017 | - | - |
547
+ | 1.5893 | 925 | 0.0505 | - | - |
548
+ | 1.5979 | 930 | 0.0341 | - | - |
549
+ | 1.6065 | 935 | 0.0223 | - | - |
550
+ | 1.6151 | 940 | 0.0469 | - | - |
551
+ | 1.6237 | 945 | 0.0386 | - | - |
552
+ | 1.6323 | 950 | 0.0214 | - | - |
553
+ | 1.6409 | 955 | 0.0329 | - | - |
554
+ | 1.6495 | 960 | 0.0398 | - | - |
555
+ | 1.6581 | 965 | 0.0355 | - | - |
556
+ | 1.6667 | 970 | 0.0373 | - | - |
557
+ | 1.6753 | 975 | 0.0339 | - | - |
558
+ | 1.6838 | 980 | 0.0349 | - | - |
559
+ | 1.6924 | 985 | 0.0439 | - | - |
560
+ | 1.7010 | 990 | 0.0425 | - | - |
561
+ | 1.7096 | 995 | 0.0318 | - | - |
562
+ | 1.7182 | 1000 | 0.025 | - | - |
563
+ | 1.7268 | 1005 | 0.0334 | - | - |
564
+ | 1.7354 | 1010 | 0.0327 | - | - |
565
+ | 1.7440 | 1015 | 0.0356 | - | - |
566
+ | 1.7526 | 1020 | 0.0428 | - | - |
567
+ | 1.7612 | 1025 | 0.0432 | - | - |
568
+ | 1.7698 | 1030 | 0.0334 | - | - |
569
+ | 1.7784 | 1035 | 0.032 | - | - |
570
+ | 1.7869 | 1040 | 0.0318 | - | - |
571
+ | 1.7955 | 1045 | 0.0281 | - | - |
572
+ | 1.8041 | 1050 | 0.0231 | - | - |
573
+ | 1.8127 | 1055 | 0.0436 | - | - |
574
+ | 1.8213 | 1060 | 0.0303 | - | - |
575
+ | 1.8299 | 1065 | 0.0489 | - | - |
576
+ | 1.8385 | 1070 | 0.0292 | - | - |
577
+ | 1.8471 | 1075 | 0.06 | - | - |
578
+ | 1.8557 | 1080 | 0.0329 | - | - |
579
+ | 1.8643 | 1085 | 0.0322 | - | - |
580
+ | 1.8729 | 1090 | 0.0426 | - | - |
581
+ | 1.8814 | 1095 | 0.0263 | - | - |
582
+ | 1.8900 | 1100 | 0.024 | - | - |
583
+ | 1.8986 | 1105 | 0.0228 | - | - |
584
+ | 1.9072 | 1110 | 0.0313 | - | - |
585
+ | 1.9158 | 1115 | 0.044 | - | - |
586
+ | 1.9244 | 1120 | 0.036 | - | - |
587
+ | 1.9330 | 1125 | 0.0252 | - | - |
588
+ | 1.9416 | 1130 | 0.0311 | - | - |
589
+ | 1.9502 | 1135 | 0.0452 | - | - |
590
+ | 1.9588 | 1140 | 0.0338 | - | - |
591
+ | 1.9674 | 1145 | 0.0447 | - | - |
592
+ | 1.9759 | 1150 | 0.0318 | - | - |
593
+ | 1.9845 | 1155 | 0.0428 | - | - |
594
+ | 1.9931 | 1160 | 0.03 | - | - |
595
+ | 2.0017 | 1165 | 0.0314 | - | - |
596
+ | 2.0103 | 1170 | 0.0181 | - | - |
597
+ | 2.0189 | 1175 | 0.0137 | - | - |
598
+ | 2.0275 | 1180 | 0.0242 | - | - |
599
+ | 2.0361 | 1185 | 0.03 | - | - |
600
+ | 2.0447 | 1190 | 0.0267 | - | - |
601
+ | 2.0533 | 1195 | 0.0263 | - | - |
602
+ | 2.0619 | 1200 | 0.0219 | 0.0392 | 0.8360 |
603
+ | 2.0704 | 1205 | 0.0189 | - | - |
604
+ | 2.0790 | 1210 | 0.0193 | - | - |
605
+ | 2.0876 | 1215 | 0.0345 | - | - |
606
+ | 2.0962 | 1220 | 0.0136 | - | - |
607
+ | 2.1048 | 1225 | 0.0346 | - | - |
608
+ | 2.1134 | 1230 | 0.0163 | - | - |
609
+ | 2.1220 | 1235 | 0.0264 | - | - |
610
+ | 2.1306 | 1240 | 0.0172 | - | - |
611
+ | 2.1392 | 1245 | 0.0163 | - | - |
612
+ | 2.1478 | 1250 | 0.0226 | - | - |
613
+ | 2.1564 | 1255 | 0.0229 | - | - |
614
+ | 2.1649 | 1260 | 0.0185 | - | - |
615
+ | 2.1735 | 1265 | 0.0134 | - | - |
616
+ | 2.1821 | 1270 | 0.0144 | - | - |
617
+ | 2.1907 | 1275 | 0.0215 | - | - |
618
+ | 2.1993 | 1280 | 0.0291 | - | - |
619
+ | 2.2079 | 1285 | 0.0305 | - | - |
620
+ | 2.2165 | 1290 | 0.0192 | - | - |
621
+ | 2.2251 | 1295 | 0.0272 | - | - |
622
+ | 2.2337 | 1300 | 0.0267 | - | - |
623
+ | 2.2423 | 1305 | 0.0265 | - | - |
624
+ | 2.2509 | 1310 | 0.0207 | - | - |
625
+ | 2.2595 | 1315 | 0.0305 | - | - |
626
+ | 2.2680 | 1320 | 0.0292 | - | - |
627
+ | 2.2766 | 1325 | 0.017 | - | - |
628
+ | 2.2852 | 1330 | 0.0242 | - | - |
629
+ | 2.2938 | 1335 | 0.016 | - | - |
630
+ | 2.3024 | 1340 | 0.0241 | - | - |
631
+ | 2.3110 | 1345 | 0.0193 | - | - |
632
+ | 2.3196 | 1350 | 0.0134 | - | - |
633
+ | 2.3282 | 1355 | 0.0206 | - | - |
634
+ | 2.3368 | 1360 | 0.0218 | - | - |
635
+ | 2.3454 | 1365 | 0.0239 | - | - |
636
+ | 2.3540 | 1370 | 0.0314 | - | - |
637
+ | 2.3625 | 1375 | 0.028 | - | - |
638
+ | 2.3711 | 1380 | 0.021 | - | - |
639
+ | 2.3797 | 1385 | 0.0179 | - | - |
640
+ | 2.3883 | 1390 | 0.0173 | - | - |
641
+ | 2.3969 | 1395 | 0.0228 | - | - |
642
+ | 2.4055 | 1400 | 0.0217 | - | - |
643
+ | 2.4141 | 1405 | 0.0243 | - | - |
644
+ | 2.4227 | 1410 | 0.018 | - | - |
645
+ | 2.4313 | 1415 | 0.0233 | - | - |
646
+ | 2.4399 | 1420 | 0.016 | - | - |
647
+ | 2.4485 | 1425 | 0.0308 | - | - |
648
+ | 2.4570 | 1430 | 0.0239 | - | - |
649
+ | 2.4656 | 1435 | 0.018 | - | - |
650
+ | 2.4742 | 1440 | 0.016 | - | - |
651
+ | 2.4828 | 1445 | 0.0189 | - | - |
652
+ | 2.4914 | 1450 | 0.0215 | - | - |
653
+ | 2.5 | 1455 | 0.027 | - | - |
654
+ | 2.5086 | 1460 | 0.0177 | - | - |
655
+ | 2.5172 | 1465 | 0.0325 | - | - |
656
+ | 2.5258 | 1470 | 0.0136 | - | - |
657
+ | 2.5344 | 1475 | 0.0235 | - | - |
658
+ | 2.5430 | 1480 | 0.0362 | - | - |
659
+ | 2.5515 | 1485 | 0.0302 | - | - |
660
+ | 2.5601 | 1490 | 0.0137 | - | - |
661
+ | 2.5687 | 1495 | 0.0162 | - | - |
662
+ | **2.5773** | **1500** | **0.0174** | **0.0376** | **0.8399** |
663
+ | 2.5859 | 1505 | 0.0248 | - | - |
664
+ | 2.5945 | 1510 | 0.0131 | - | - |
665
+ | 2.6031 | 1515 | 0.0188 | - | - |
666
+ | 2.6117 | 1520 | 0.011 | - | - |
667
+ | 2.6203 | 1525 | 0.0174 | - | - |
668
+ | 2.6289 | 1530 | 0.0192 | - | - |
669
+ | 2.6375 | 1535 | 0.0113 | - | - |
670
+ | 2.6460 | 1540 | 0.0304 | - | - |
671
+ | 2.6546 | 1545 | 0.0217 | - | - |
672
+ | 2.6632 | 1550 | 0.0102 | - | - |
673
+ | 2.6718 | 1555 | 0.0164 | - | - |
674
+ | 2.6804 | 1560 | 0.017 | - | - |
675
+ | 2.6890 | 1565 | 0.0146 | - | - |
676
+ | 2.6976 | 1570 | 0.0139 | - | - |
677
+ | 2.7062 | 1575 | 0.0171 | - | - |
678
+ | 2.7148 | 1580 | 0.0137 | - | - |
679
+ | 2.7234 | 1585 | 0.008 | - | - |
680
+ | 2.7320 | 1590 | 0.0222 | - | - |
681
+ | 2.7405 | 1595 | 0.0295 | - | - |
682
+ | 2.7491 | 1600 | 0.0178 | - | - |
683
+ | 2.7577 | 1605 | 0.0144 | - | - |
684
+ | 2.7663 | 1610 | 0.023 | - | - |
685
+ | 2.7749 | 1615 | 0.0135 | - | - |
686
+ | 2.7835 | 1620 | 0.0213 | - | - |
687
+ | 2.7921 | 1625 | 0.0213 | - | - |
688
+ | 2.8007 | 1630 | 0.0212 | - | - |
689
+ | 2.8093 | 1635 | 0.0164 | - | - |
690
+ | 2.8179 | 1640 | 0.0212 | - | - |
691
+ | 2.8265 | 1645 | 0.0157 | - | - |
692
+ | 2.8351 | 1650 | 0.0251 | - | - |
693
+ | 2.8436 | 1655 | 0.0276 | - | - |
694
+ | 2.8522 | 1660 | 0.0104 | - | - |
695
+ | 2.8608 | 1665 | 0.0123 | - | - |
696
+ | 2.8694 | 1670 | 0.0339 | - | - |
697
+ | 2.8780 | 1675 | 0.0203 | - | - |
698
+ | 2.8866 | 1680 | 0.0171 | - | - |
699
+ | 2.8952 | 1685 | 0.0304 | - | - |
700
+ | 2.9038 | 1690 | 0.015 | - | - |
701
+ | 2.9124 | 1695 | 0.0177 | - | - |
702
+ | 2.9210 | 1700 | 0.0176 | - | - |
703
+ | 2.9296 | 1705 | 0.0229 | - | - |
704
+ | 2.9381 | 1710 | 0.0166 | - | - |
705
+ | 2.9467 | 1715 | 0.0185 | - | - |
706
+ | 2.9553 | 1720 | 0.017 | - | - |
707
+ | 2.9639 | 1725 | 0.0109 | - | - |
708
+ | 2.9725 | 1730 | 0.0154 | - | - |
709
+ | 2.9811 | 1735 | 0.0226 | - | - |
710
+ | 2.9897 | 1740 | 0.0142 | - | - |
711
+ | 2.9983 | 1745 | 0.0257 | - | - |
712
+
713
+ * The bold row denotes the saved checkpoint.
714
+ </details>
715
+
716
+ ### Framework Versions
717
+ - Python: 3.10.12
718
+ - Sentence Transformers: 3.3.1
719
+ - Transformers: 4.47.1
720
+ - PyTorch: 2.5.1+cu121
721
+ - Accelerate: 1.2.1
722
+ - Datasets: 2.14.4
723
+ - Tokenizers: 0.21.0
724
+
725
+ ## Citation
726
+
727
+ ### BibTeX
728
+
729
+ #### Sentence Transformers
730
+ ```bibtex
731
+ @inproceedings{reimers-2019-sentence-bert,
732
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
733
+ author = "Reimers, Nils and Gurevych, Iryna",
734
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
735
+ month = "11",
736
+ year = "2019",
737
+ publisher = "Association for Computational Linguistics",
738
+ url = "https://arxiv.org/abs/1908.10084",
739
+ }
740
+ ```
741
+
742
+ <!--
743
+ ## Glossary
744
+
745
+ *Clearly define terms in order to be accessible across audiences.*
746
+ -->
747
+
748
+ <!--
749
+ ## Model Card Authors
750
+
751
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
752
+ -->
753
+
754
+ <!--
755
+ ## Model Card Contact
756
+
757
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
758
+ -->
config.json ADDED
@@ -0,0 +1,32 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "_name_or_path": "/content/models/bert-ptbr-e3-lr0.0001",
3
+ "architectures": [
4
+ "BertModel"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
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+ "classifier_dropout": null,
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+ "directionality": "bidi",
9
+ "hidden_act": "gelu",
10
+ "hidden_dropout_prob": 0.1,
11
+ "hidden_size": 1024,
12
+ "initializer_range": 0.02,
13
+ "intermediate_size": 4096,
14
+ "layer_norm_eps": 1e-12,
15
+ "max_position_embeddings": 512,
16
+ "model_type": "bert",
17
+ "num_attention_heads": 16,
18
+ "num_hidden_layers": 24,
19
+ "output_past": true,
20
+ "pad_token_id": 0,
21
+ "pooler_fc_size": 768,
22
+ "pooler_num_attention_heads": 12,
23
+ "pooler_num_fc_layers": 3,
24
+ "pooler_size_per_head": 128,
25
+ "pooler_type": "first_token_transform",
26
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