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SenhorDasMoscas
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acho-ptbr-e3-lr0.0001-08-01-2025

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:19697
loss:CosineSimilarityLoss
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
SenhorDasMoscas commited on
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1 Parent(s): de24595

Add new SentenceTransformer model.

Browse files
Files changed (11) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +782 -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
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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:19697
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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: procurar sapato social masculino
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+ sentences:
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+ - beleza autocuidado
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+ - moda acessorio
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+ - doce chocolate
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+ - source_sentence: livro ultimo adeus cynthia hand
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+ sentences:
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+ - livro material literario
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+ - item colecao
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+ - joia bijuterio
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+ - source_sentence: relogio pulso
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+ sentences:
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+ - servico reparo eletronico
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+ - hortifruti
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+ - hortifruti
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+ - source_sentence: medicamento antipulga gato
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+ sentences:
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+ - produto pet animal domestico
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+ - hortifruti
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+ - padaria confeitaria
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+ - source_sentence: guitarra gibson Les Paul
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+ sentences:
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+ - moda acessorio
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+ - tinta
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+ - peixaria pescado
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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: 0.932130151806209
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.8467496824207882
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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)
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+
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+ First install the Sentence Transformers library:
95
+
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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.
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+ ```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/acho-ptbr-e3-lr0.0001-08-01-2025")
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+ # Run inference
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+ sentences = [
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+ 'guitarra gibson Les Paul',
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+ 'tinta',
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+ 'peixaria pescado',
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+ ]
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+ embeddings = model.encode(sentences)
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+ print(embeddings.shape)
114
+ # [3, 1024]
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+
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+ # Get the similarity scores for the embeddings
117
+ similarities = model.similarity(embeddings, embeddings)
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+ print(similarities.shape)
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+ # [3, 3]
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+ ```
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+
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+ <!--
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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>
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+
137
+ </details>
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+ -->
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+
140
+ <!--
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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.*
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)
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+
155
+ | Metric | Value |
156
+ |:--------------------|:-----------|
157
+ | pearson_cosine | 0.9321 |
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+ | **spearman_cosine** | **0.8467** |
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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: 19,697 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 |
185
+ | details | <ul><li>min: 3 tokens</li><li>mean: 7.78 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.17 tokens</li><li>max: 11 tokens</li></ul> | <ul><li>min: 0.1</li><li>mean: 0.55</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>fritadeira eletrico em esse loja festa</code> | <code>decoracao festa</code> | <code>0.1</code> |
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+ | <code>vinho</code> | <code>papelaria escritorio</code> | <code>0.1</code> |
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+ | <code>forno eletrico Fischer</code> | <code>eletrodomestico</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:
193
+ ```json
194
+ {
195
+ "loss_fct": "torch.nn.modules.loss.MSELoss"
196
+ }
197
+ ```
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+
199
+ ### Evaluation Dataset
200
+
201
+ #### Unnamed Dataset
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+
203
+
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+ * Size: 2,189 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 |
208
+ |:--------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
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+ | type | string | string | float |
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+ | details | <ul><li>min: 3 tokens</li><li>mean: 7.7 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.16 tokens</li><li>max: 11 tokens</li></ul> | <ul><li>min: 0.1</li><li>mean: 0.52</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>querer salgado</code> | <code>comida rapido fastfood</code> | <code>1.0</code> |
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+ | <code>ervilha enlatar</code> | <code>movel</code> | <code>0.1</code> |
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+ | <code>preciso loja artigo esporte aquatico</code> | <code>servico area educacao</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
219
+ {
220
+ "loss_fct": "torch.nn.modules.loss.MSELoss"
221
+ }
222
+ ```
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+
224
+ ### Training Hyperparameters
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+ #### Non-Default Hyperparameters
226
+
227
+ - `eval_strategy`: steps
228
+ - `per_device_train_batch_size`: 32
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+ - `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`: 246
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+ - `fp16`: True
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+ - `load_best_model_at_end`: True
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+
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`: 246
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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
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+ - `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
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+ - `gradient_checkpointing`: False
325
+ - `gradient_checkpointing_kwargs`: None
326
+ - `include_inputs_for_metrics`: False
327
+ - `include_for_metrics`: []
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+ - `eval_do_concat_batches`: True
329
+ - `fp16_backend`: auto
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+ - `push_to_hub_model_id`: None
331
+ - `push_to_hub_organization`: None
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+ - `mp_parameters`:
333
+ - `auto_find_batch_size`: False
334
+ - `full_determinism`: False
335
+ - `torchdynamo`: None
336
+ - `ray_scope`: last
337
+ - `ddp_timeout`: 1800
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+ - `torch_compile`: False
339
+ - `torch_compile_backend`: None
340
+ - `torch_compile_mode`: None
341
+ - `dispatch_batches`: None
342
+ - `split_batches`: None
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+ - `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
+ | 0.0081 | 5 | 0.1965 | - | - |
364
+ | 0.0162 | 10 | 0.2125 | - | - |
365
+ | 0.0244 | 15 | 0.1944 | - | - |
366
+ | 0.0325 | 20 | 0.1674 | - | - |
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+ | 0.0406 | 25 | 0.1518 | - | - |
368
+ | 0.0487 | 30 | 0.1381 | - | - |
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+ | 0.0568 | 35 | 0.1385 | - | - |
370
+ | 0.0649 | 40 | 0.109 | - | - |
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+ | 0.0731 | 45 | 0.1054 | - | - |
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+ | 0.0812 | 50 | 0.0963 | - | - |
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+ | 0.0893 | 55 | 0.0917 | - | - |
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+ | 0.0974 | 60 | 0.0797 | - | - |
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+ | 0.1055 | 65 | 0.0877 | - | - |
376
+ | 0.1136 | 70 | 0.0755 | - | - |
377
+ | 0.1218 | 75 | 0.0773 | - | - |
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+ | 0.1299 | 80 | 0.0605 | - | - |
379
+ | 0.1380 | 85 | 0.0669 | - | - |
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+ | 0.1461 | 90 | 0.0698 | - | - |
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+ | 0.1542 | 95 | 0.0595 | - | - |
382
+ | 0.1623 | 100 | 0.0382 | - | - |
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+ | 0.1705 | 105 | 0.0723 | - | - |
384
+ | 0.1786 | 110 | 0.0448 | - | - |
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+ | 0.1867 | 115 | 0.0703 | - | - |
386
+ | 0.1948 | 120 | 0.0694 | - | - |
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+ | 0.2029 | 125 | 0.0515 | - | - |
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+ | 0.2110 | 130 | 0.0581 | - | - |
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+ | 0.2192 | 135 | 0.0458 | - | - |
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+ | 0.2273 | 140 | 0.0643 | - | - |
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+ | 0.2354 | 145 | 0.0602 | - | - |
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+ | 0.2435 | 150 | 0.0651 | - | - |
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+ | 0.2516 | 155 | 0.0662 | - | - |
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+ | 0.2597 | 160 | 0.0712 | - | - |
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+ | 0.2679 | 165 | 0.0546 | - | - |
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+ | 0.2760 | 170 | 0.0419 | - | - |
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+ | 0.2841 | 175 | 0.061 | - | - |
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+ | 0.2922 | 180 | 0.0549 | - | - |
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+ | 0.3003 | 185 | 0.0523 | - | - |
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+ | 0.3084 | 190 | 0.0579 | - | - |
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+ | 0.3166 | 195 | 0.0595 | - | - |
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+ | 0.3247 | 200 | 0.0478 | - | - |
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+ | 0.3328 | 205 | 0.0507 | - | - |
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+ | 0.3409 | 210 | 0.0312 | - | - |
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+ | 0.3490 | 215 | 0.041 | - | - |
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+ | 0.3571 | 220 | 0.0528 | - | - |
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+ | 0.3653 | 225 | 0.0386 | - | - |
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+ | 0.3734 | 230 | 0.0656 | - | - |
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+ | 0.3815 | 235 | 0.0567 | - | - |
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+ | 0.3896 | 240 | 0.0673 | - | - |
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+ | 0.3977 | 245 | 0.103 | - | - |
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+ | 0.4058 | 250 | 0.1704 | - | - |
413
+ | 0.4140 | 255 | 0.0844 | - | - |
414
+ | 0.4221 | 260 | 0.0883 | - | - |
415
+ | 0.4302 | 265 | 0.0728 | - | - |
416
+ | 0.4383 | 270 | 0.0531 | - | - |
417
+ | 0.4464 | 275 | 0.0715 | - | - |
418
+ | 0.4545 | 280 | 0.0623 | - | - |
419
+ | 0.4627 | 285 | 0.0679 | - | - |
420
+ | 0.4708 | 290 | 0.0577 | - | - |
421
+ | 0.4789 | 295 | 0.0781 | - | - |
422
+ | 0.4870 | 300 | 0.0541 | - | - |
423
+ | 0.4951 | 305 | 0.0876 | - | - |
424
+ | 0.5032 | 310 | 0.0648 | - | - |
425
+ | 0.5114 | 315 | 0.0583 | - | - |
426
+ | 0.5195 | 320 | 0.0506 | - | - |
427
+ | 0.5276 | 325 | 0.051 | - | - |
428
+ | 0.5357 | 330 | 0.0633 | - | - |
429
+ | 0.5438 | 335 | 0.0764 | - | - |
430
+ | 0.5519 | 340 | 0.0753 | - | - |
431
+ | 0.5601 | 345 | 0.0701 | - | - |
432
+ | 0.5682 | 350 | 0.0688 | - | - |
433
+ | 0.5763 | 355 | 0.0691 | - | - |
434
+ | 0.5844 | 360 | 0.0497 | - | - |
435
+ | 0.5925 | 365 | 0.0606 | - | - |
436
+ | 0.6006 | 370 | 0.0544 | - | - |
437
+ | 0.6088 | 375 | 0.0587 | - | - |
438
+ | 0.6169 | 380 | 0.0432 | - | - |
439
+ | 0.625 | 385 | 0.0768 | - | - |
440
+ | 0.6331 | 390 | 0.0701 | - | - |
441
+ | 0.6412 | 395 | 0.0421 | - | - |
442
+ | 0.6494 | 400 | 0.0415 | - | - |
443
+ | 0.6575 | 405 | 0.0419 | - | - |
444
+ | 0.6656 | 410 | 0.0613 | - | - |
445
+ | 0.6737 | 415 | 0.0442 | - | - |
446
+ | 0.6818 | 420 | 0.0487 | - | - |
447
+ | 0.6899 | 425 | 0.0443 | - | - |
448
+ | 0.6981 | 430 | 0.0493 | - | - |
449
+ | 0.7062 | 435 | 0.0429 | - | - |
450
+ | 0.7143 | 440 | 0.0464 | - | - |
451
+ | 0.7224 | 445 | 0.0541 | - | - |
452
+ | 0.7305 | 450 | 0.0539 | - | - |
453
+ | 0.7386 | 455 | 0.0497 | - | - |
454
+ | 0.7468 | 460 | 0.0471 | - | - |
455
+ | 0.75 | 462 | - | 0.0457 | 0.8234 |
456
+ | 0.7549 | 465 | 0.0514 | - | - |
457
+ | 0.7630 | 470 | 0.0457 | - | - |
458
+ | 0.7711 | 475 | 0.0315 | - | - |
459
+ | 0.7792 | 480 | 0.0491 | - | - |
460
+ | 0.7873 | 485 | 0.0619 | - | - |
461
+ | 0.7955 | 490 | 0.0298 | - | - |
462
+ | 0.8036 | 495 | 0.0725 | - | - |
463
+ | 0.8117 | 500 | 0.043 | - | - |
464
+ | 0.8198 | 505 | 0.0392 | - | - |
465
+ | 0.8279 | 510 | 0.0275 | - | - |
466
+ | 0.8360 | 515 | 0.0509 | - | - |
467
+ | 0.8442 | 520 | 0.0508 | - | - |
468
+ | 0.8523 | 525 | 0.0394 | - | - |
469
+ | 0.8604 | 530 | 0.0309 | - | - |
470
+ | 0.8685 | 535 | 0.0601 | - | - |
471
+ | 0.8766 | 540 | 0.0524 | - | - |
472
+ | 0.8847 | 545 | 0.0491 | - | - |
473
+ | 0.8929 | 550 | 0.0626 | - | - |
474
+ | 0.9010 | 555 | 0.0395 | - | - |
475
+ | 0.9091 | 560 | 0.0655 | - | - |
476
+ | 0.9172 | 565 | 0.045 | - | - |
477
+ | 0.9253 | 570 | 0.0394 | - | - |
478
+ | 0.9334 | 575 | 0.0521 | - | - |
479
+ | 0.9416 | 580 | 0.0324 | - | - |
480
+ | 0.9497 | 585 | 0.0426 | - | - |
481
+ | 0.9578 | 590 | 0.032 | - | - |
482
+ | 0.9659 | 595 | 0.0425 | - | - |
483
+ | 0.9740 | 600 | 0.0458 | - | - |
484
+ | 0.9821 | 605 | 0.0341 | - | - |
485
+ | 0.9903 | 610 | 0.0339 | - | - |
486
+ | 0.9984 | 615 | 0.0444 | - | - |
487
+ | 1.0065 | 620 | 0.0364 | - | - |
488
+ | 1.0146 | 625 | 0.0277 | - | - |
489
+ | 1.0227 | 630 | 0.0372 | - | - |
490
+ | 1.0308 | 635 | 0.0254 | - | - |
491
+ | 1.0390 | 640 | 0.0382 | - | - |
492
+ | 1.0471 | 645 | 0.0333 | - | - |
493
+ | 1.0552 | 650 | 0.0312 | - | - |
494
+ | 1.0633 | 655 | 0.0366 | - | - |
495
+ | 1.0714 | 660 | 0.0341 | - | - |
496
+ | 1.0795 | 665 | 0.0146 | - | - |
497
+ | 1.0877 | 670 | 0.0362 | - | - |
498
+ | 1.0958 | 675 | 0.0225 | - | - |
499
+ | 1.1039 | 680 | 0.038 | - | - |
500
+ | 1.1120 | 685 | 0.0406 | - | - |
501
+ | 1.1201 | 690 | 0.0392 | - | - |
502
+ | 1.1282 | 695 | 0.0343 | - | - |
503
+ | 1.1364 | 700 | 0.0494 | - | - |
504
+ | 1.1445 | 705 | 0.021 | - | - |
505
+ | 1.1526 | 710 | 0.0358 | - | - |
506
+ | 1.1607 | 715 | 0.034 | - | - |
507
+ | 1.1688 | 720 | 0.0288 | - | - |
508
+ | 1.1769 | 725 | 0.0224 | - | - |
509
+ | 1.1851 | 730 | 0.0324 | - | - |
510
+ | 1.1932 | 735 | 0.0378 | - | - |
511
+ | 1.2013 | 740 | 0.0446 | - | - |
512
+ | 1.2094 | 745 | 0.0293 | - | - |
513
+ | 1.2175 | 750 | 0.0314 | - | - |
514
+ | 1.2256 | 755 | 0.0444 | - | - |
515
+ | 1.2338 | 760 | 0.0283 | - | - |
516
+ | 1.2419 | 765 | 0.0207 | - | - |
517
+ | 1.25 | 770 | 0.0413 | - | - |
518
+ | 1.2581 | 775 | 0.0317 | - | - |
519
+ | 1.2662 | 780 | 0.0382 | - | - |
520
+ | 1.2744 | 785 | 0.0363 | - | - |
521
+ | 1.2825 | 790 | 0.0324 | - | - |
522
+ | 1.2906 | 795 | 0.0225 | - | - |
523
+ | 1.2987 | 800 | 0.0316 | - | - |
524
+ | 1.3068 | 805 | 0.0438 | - | - |
525
+ | 1.3149 | 810 | 0.0298 | - | - |
526
+ | 1.3231 | 815 | 0.0395 | - | - |
527
+ | 1.3312 | 820 | 0.0388 | - | - |
528
+ | 1.3393 | 825 | 0.0289 | - | - |
529
+ | 1.3474 | 830 | 0.0233 | - | - |
530
+ | 1.3555 | 835 | 0.022 | - | - |
531
+ | 1.3636 | 840 | 0.016 | - | - |
532
+ | 1.3718 | 845 | 0.0488 | - | - |
533
+ | 1.3799 | 850 | 0.0519 | - | - |
534
+ | 1.3880 | 855 | 0.033 | - | - |
535
+ | 1.3961 | 860 | 0.025 | - | - |
536
+ | 1.4042 | 865 | 0.0212 | - | - |
537
+ | 1.4123 | 870 | 0.0184 | - | - |
538
+ | 1.4205 | 875 | 0.0335 | - | - |
539
+ | 1.4286 | 880 | 0.0308 | - | - |
540
+ | 1.4367 | 885 | 0.028 | - | - |
541
+ | 1.4448 | 890 | 0.0352 | - | - |
542
+ | 1.4529 | 895 | 0.0255 | - | - |
543
+ | 1.4610 | 900 | 0.0243 | - | - |
544
+ | 1.4692 | 905 | 0.0355 | - | - |
545
+ | 1.4773 | 910 | 0.0267 | - | - |
546
+ | 1.4854 | 915 | 0.0263 | - | - |
547
+ | 1.4935 | 920 | 0.0275 | - | - |
548
+ | 1.5 | 924 | - | 0.0313 | 0.8414 |
549
+ | 1.5016 | 925 | 0.0294 | - | - |
550
+ | 1.5097 | 930 | 0.0514 | - | - |
551
+ | 1.5179 | 935 | 0.0321 | - | - |
552
+ | 1.5260 | 940 | 0.0306 | - | - |
553
+ | 1.5341 | 945 | 0.0279 | - | - |
554
+ | 1.5422 | 950 | 0.0334 | - | - |
555
+ | 1.5503 | 955 | 0.0337 | - | - |
556
+ | 1.5584 | 960 | 0.0266 | - | - |
557
+ | 1.5666 | 965 | 0.036 | - | - |
558
+ | 1.5747 | 970 | 0.0328 | - | - |
559
+ | 1.5828 | 975 | 0.0224 | - | - |
560
+ | 1.5909 | 980 | 0.0404 | - | - |
561
+ | 1.5990 | 985 | 0.0293 | - | - |
562
+ | 1.6071 | 990 | 0.016 | - | - |
563
+ | 1.6153 | 995 | 0.0177 | - | - |
564
+ | 1.6234 | 1000 | 0.0216 | - | - |
565
+ | 1.6315 | 1005 | 0.029 | - | - |
566
+ | 1.6396 | 1010 | 0.0306 | - | - |
567
+ | 1.6477 | 1015 | 0.0291 | - | - |
568
+ | 1.6558 | 1020 | 0.032 | - | - |
569
+ | 1.6640 | 1025 | 0.0277 | - | - |
570
+ | 1.6721 | 1030 | 0.0191 | - | - |
571
+ | 1.6802 | 1035 | 0.0353 | - | - |
572
+ | 1.6883 | 1040 | 0.0304 | - | - |
573
+ | 1.6964 | 1045 | 0.0385 | - | - |
574
+ | 1.7045 | 1050 | 0.0315 | - | - |
575
+ | 1.7127 | 1055 | 0.0428 | - | - |
576
+ | 1.7208 | 1060 | 0.0338 | - | - |
577
+ | 1.7289 | 1065 | 0.0258 | - | - |
578
+ | 1.7370 | 1070 | 0.0303 | - | - |
579
+ | 1.7451 | 1075 | 0.0171 | - | - |
580
+ | 1.7532 | 1080 | 0.0229 | - | - |
581
+ | 1.7614 | 1085 | 0.0278 | - | - |
582
+ | 1.7695 | 1090 | 0.0246 | - | - |
583
+ | 1.7776 | 1095 | 0.0241 | - | - |
584
+ | 1.7857 | 1100 | 0.0182 | - | - |
585
+ | 1.7938 | 1105 | 0.0366 | - | - |
586
+ | 1.8019 | 1110 | 0.0204 | - | - |
587
+ | 1.8101 | 1115 | 0.0208 | - | - |
588
+ | 1.8182 | 1120 | 0.01 | - | - |
589
+ | 1.8263 | 1125 | 0.0239 | - | - |
590
+ | 1.8344 | 1130 | 0.0228 | - | - |
591
+ | 1.8425 | 1135 | 0.0228 | - | - |
592
+ | 1.8506 | 1140 | 0.0176 | - | - |
593
+ | 1.8588 | 1145 | 0.0278 | - | - |
594
+ | 1.8669 | 1150 | 0.0242 | - | - |
595
+ | 1.875 | 1155 | 0.0174 | - | - |
596
+ | 1.8831 | 1160 | 0.0248 | - | - |
597
+ | 1.8912 | 1165 | 0.0192 | - | - |
598
+ | 1.8994 | 1170 | 0.0293 | - | - |
599
+ | 1.9075 | 1175 | 0.017 | - | - |
600
+ | 1.9156 | 1180 | 0.0212 | - | - |
601
+ | 1.9237 | 1185 | 0.0214 | - | - |
602
+ | 1.9318 | 1190 | 0.025 | - | - |
603
+ | 1.9399 | 1195 | 0.0246 | - | - |
604
+ | 1.9481 | 1200 | 0.0202 | - | - |
605
+ | 1.9562 | 1205 | 0.021 | - | - |
606
+ | 1.9643 | 1210 | 0.0183 | - | - |
607
+ | 1.9724 | 1215 | 0.0313 | - | - |
608
+ | 1.9805 | 1220 | 0.0211 | - | - |
609
+ | 1.9886 | 1225 | 0.0299 | - | - |
610
+ | 1.9968 | 1230 | 0.0222 | - | - |
611
+ | 2.0049 | 1235 | 0.0154 | - | - |
612
+ | 2.0130 | 1240 | 0.018 | - | - |
613
+ | 2.0211 | 1245 | 0.0212 | - | - |
614
+ | 2.0292 | 1250 | 0.0123 | - | - |
615
+ | 2.0373 | 1255 | 0.013 | - | - |
616
+ | 2.0455 | 1260 | 0.0213 | - | - |
617
+ | 2.0536 | 1265 | 0.0125 | - | - |
618
+ | 2.0617 | 1270 | 0.0175 | - | - |
619
+ | 2.0698 | 1275 | 0.0092 | - | - |
620
+ | 2.0779 | 1280 | 0.0209 | - | - |
621
+ | 2.0860 | 1285 | 0.0135 | - | - |
622
+ | 2.0942 | 1290 | 0.0295 | - | - |
623
+ | 2.1023 | 1295 | 0.0175 | - | - |
624
+ | 2.1104 | 1300 | 0.0252 | - | - |
625
+ | 2.1185 | 1305 | 0.0071 | - | - |
626
+ | 2.1266 | 1310 | 0.0139 | - | - |
627
+ | 2.1347 | 1315 | 0.0104 | - | - |
628
+ | 2.1429 | 1320 | 0.0125 | - | - |
629
+ | 2.1510 | 1325 | 0.0103 | - | - |
630
+ | 2.1591 | 1330 | 0.0171 | - | - |
631
+ | 2.1672 | 1335 | 0.0083 | - | - |
632
+ | 2.1753 | 1340 | 0.0185 | - | - |
633
+ | 2.1834 | 1345 | 0.0141 | - | - |
634
+ | 2.1916 | 1350 | 0.0177 | - | - |
635
+ | 2.1997 | 1355 | 0.0189 | - | - |
636
+ | 2.2078 | 1360 | 0.0254 | - | - |
637
+ | 2.2159 | 1365 | 0.0198 | - | - |
638
+ | 2.2240 | 1370 | 0.0162 | - | - |
639
+ | 2.2321 | 1375 | 0.0139 | - | - |
640
+ | 2.2403 | 1380 | 0.013 | - | - |
641
+ | 2.2484 | 1385 | 0.0201 | - | - |
642
+ | 2.25 | 1386 | - | 0.0292 | 0.8443 |
643
+ | 2.2565 | 1390 | 0.0202 | - | - |
644
+ | 2.2646 | 1395 | 0.0169 | - | - |
645
+ | 2.2727 | 1400 | 0.0105 | - | - |
646
+ | 2.2808 | 1405 | 0.0136 | - | - |
647
+ | 2.2890 | 1410 | 0.0125 | - | - |
648
+ | 2.2971 | 1415 | 0.0168 | - | - |
649
+ | 2.3052 | 1420 | 0.0108 | - | - |
650
+ | 2.3133 | 1425 | 0.0297 | - | - |
651
+ | 2.3214 | 1430 | 0.0233 | - | - |
652
+ | 2.3295 | 1435 | 0.0164 | - | - |
653
+ | 2.3377 | 1440 | 0.0178 | - | - |
654
+ | 2.3458 | 1445 | 0.0203 | - | - |
655
+ | 2.3539 | 1450 | 0.0112 | - | - |
656
+ | 2.3620 | 1455 | 0.0156 | - | - |
657
+ | 2.3701 | 1460 | 0.0151 | - | - |
658
+ | 2.3782 | 1465 | 0.0097 | - | - |
659
+ | 2.3864 | 1470 | 0.0196 | - | - |
660
+ | 2.3945 | 1475 | 0.0148 | - | - |
661
+ | 2.4026 | 1480 | 0.0154 | - | - |
662
+ | 2.4107 | 1485 | 0.0069 | - | - |
663
+ | 2.4188 | 1490 | 0.0145 | - | - |
664
+ | 2.4269 | 1495 | 0.0204 | - | - |
665
+ | 2.4351 | 1500 | 0.0225 | - | - |
666
+ | 2.4432 | 1505 | 0.0165 | - | - |
667
+ | 2.4513 | 1510 | 0.0079 | - | - |
668
+ | 2.4594 | 1515 | 0.0183 | - | - |
669
+ | 2.4675 | 1520 | 0.0196 | - | - |
670
+ | 2.4756 | 1525 | 0.0085 | - | - |
671
+ | 2.4838 | 1530 | 0.0109 | - | - |
672
+ | 2.4919 | 1535 | 0.0168 | - | - |
673
+ | 2.5 | 1540 | 0.0124 | - | - |
674
+ | 2.5081 | 1545 | 0.0218 | - | - |
675
+ | 2.5162 | 1550 | 0.0164 | - | - |
676
+ | 2.5244 | 1555 | 0.0234 | - | - |
677
+ | 2.5325 | 1560 | 0.0115 | - | - |
678
+ | 2.5406 | 1565 | 0.0135 | - | - |
679
+ | 2.5487 | 1570 | 0.0179 | - | - |
680
+ | 2.5568 | 1575 | 0.0104 | - | - |
681
+ | 2.5649 | 1580 | 0.0188 | - | - |
682
+ | 2.5731 | 1585 | 0.0166 | - | - |
683
+ | 2.5812 | 1590 | 0.0228 | - | - |
684
+ | 2.5893 | 1595 | 0.015 | - | - |
685
+ | 2.5974 | 1600 | 0.0171 | - | - |
686
+ | 2.6055 | 1605 | 0.0207 | - | - |
687
+ | 2.6136 | 1610 | 0.009 | - | - |
688
+ | 2.6218 | 1615 | 0.0111 | - | - |
689
+ | 2.6299 | 1620 | 0.0109 | - | - |
690
+ | 2.6380 | 1625 | 0.0175 | - | - |
691
+ | 2.6461 | 1630 | 0.0155 | - | - |
692
+ | 2.6542 | 1635 | 0.0193 | - | - |
693
+ | 2.6623 | 1640 | 0.0189 | - | - |
694
+ | 2.6705 | 1645 | 0.0123 | - | - |
695
+ | 2.6786 | 1650 | 0.0102 | - | - |
696
+ | 2.6867 | 1655 | 0.0097 | - | - |
697
+ | 2.6948 | 1660 | 0.0116 | - | - |
698
+ | 2.7029 | 1665 | 0.0134 | - | - |
699
+ | 2.7110 | 1670 | 0.0218 | - | - |
700
+ | 2.7192 | 1675 | 0.0148 | - | - |
701
+ | 2.7273 | 1680 | 0.0137 | - | - |
702
+ | 2.7354 | 1685 | 0.0062 | - | - |
703
+ | 2.7435 | 1690 | 0.0075 | - | - |
704
+ | 2.7516 | 1695 | 0.0078 | - | - |
705
+ | 2.7597 | 1700 | 0.0151 | - | - |
706
+ | 2.7679 | 1705 | 0.0157 | - | - |
707
+ | 2.7760 | 1710 | 0.0153 | - | - |
708
+ | 2.7841 | 1715 | 0.0088 | - | - |
709
+ | 2.7922 | 1720 | 0.0093 | - | - |
710
+ | 2.8003 | 1725 | 0.0154 | - | - |
711
+ | 2.8084 | 1730 | 0.0124 | - | - |
712
+ | 2.8166 | 1735 | 0.0128 | - | - |
713
+ | 2.8247 | 1740 | 0.0088 | - | - |
714
+ | 2.8328 | 1745 | 0.0144 | - | - |
715
+ | 2.8409 | 1750 | 0.0184 | - | - |
716
+ | 2.8490 | 1755 | 0.0114 | - | - |
717
+ | 2.8571 | 1760 | 0.0043 | - | - |
718
+ | 2.8653 | 1765 | 0.0151 | - | - |
719
+ | 2.8734 | 1770 | 0.0089 | - | - |
720
+ | 2.8815 | 1775 | 0.014 | - | - |
721
+ | 2.8896 | 1780 | 0.0095 | - | - |
722
+ | 2.8977 | 1785 | 0.0106 | - | - |
723
+ | 2.9058 | 1790 | 0.007 | - | - |
724
+ | 2.9140 | 1795 | 0.0275 | - | - |
725
+ | 2.9221 | 1800 | 0.0185 | - | - |
726
+ | 2.9302 | 1805 | 0.0158 | - | - |
727
+ | 2.9383 | 1810 | 0.0134 | - | - |
728
+ | 2.9464 | 1815 | 0.0068 | - | - |
729
+ | 2.9545 | 1820 | 0.0144 | - | - |
730
+ | 2.9627 | 1825 | 0.0134 | - | - |
731
+ | 2.9708 | 1830 | 0.0109 | - | - |
732
+ | 2.9789 | 1835 | 0.0114 | - | - |
733
+ | 2.9870 | 1840 | 0.0097 | - | - |
734
+ | 2.9951 | 1845 | 0.0076 | - | - |
735
+ | **3.0** | **1848** | **-** | **0.0269** | **0.8467** |
736
+
737
+ * The bold row denotes the saved checkpoint.
738
+ </details>
739
+
740
+ ### Framework Versions
741
+ - Python: 3.10.12
742
+ - Sentence Transformers: 3.3.1
743
+ - Transformers: 4.47.1
744
+ - PyTorch: 2.5.1+cu121
745
+ - Accelerate: 1.2.1
746
+ - Datasets: 2.14.4
747
+ - Tokenizers: 0.21.0
748
+
749
+ ## Citation
750
+
751
+ ### BibTeX
752
+
753
+ #### Sentence Transformers
754
+ ```bibtex
755
+ @inproceedings{reimers-2019-sentence-bert,
756
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
757
+ author = "Reimers, Nils and Gurevych, Iryna",
758
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
759
+ month = "11",
760
+ year = "2019",
761
+ publisher = "Association for Computational Linguistics",
762
+ url = "https://arxiv.org/abs/1908.10084",
763
+ }
764
+ ```
765
+
766
+ <!--
767
+ ## Glossary
768
+
769
+ *Clearly define terms in order to be accessible across audiences.*
770
+ -->
771
+
772
+ <!--
773
+ ## Model Card Authors
774
+
775
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
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