milnico commited on
Commit
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Add new SentenceTransformer model.

Browse files
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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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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+ base_model: sentence-transformers/all-mpnet-base-v2
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+ datasets: []
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+ language: []
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+ library_name: sentence-transformers
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+ pipeline_tag: sentence-similarity
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+ tags:
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+ - sentence-transformers
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+ - sentence-similarity
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+ - feature-extraction
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+ - generated_from_trainer
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+ - dataset_size:100000
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+ - loss:CosineSimilarityLoss
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+ widget:
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+ - source_sentence: Believe that unfortunate events occur because of bad luck.
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+ sentences:
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+ - Had someone over for dinner.
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+ - Avoid difficult reading material.
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+ - Bought or picked flowers.
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+ - source_sentence: Enjoy thinking about things.
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+ sentences:
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+ - Had the experience of being in a familiar place but finding it strange and unfamiliar.
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+ - Express childlike joy.
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+ - Do just enough work to get by.
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+ - source_sentence: Sympathize with the homeless.
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+ sentences:
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+ - Want to be told I am right.
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+ - Act without thinking.
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+ - Had a poor appetite.
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+ - source_sentence: Avoid philosophical discussions.
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+ sentences:
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+ - Start conversations.
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+ - Radiate joy.
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+ - Am on good terms with nearly everyone.
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+ - source_sentence: Let others make the decisions.
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+ sentences:
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+ - Begin to panic when there is danger.
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+ - Believe there are many sides to most issues.
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+ - Must try to maintain harmony within my group.
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+ ---
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+
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+ # SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). It maps sentences & paragraphs to a 768-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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision 84f2bcc00d77236f9e89c8a360a00fb1139bf47d -->
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+ - **Maximum Sequence Length:** 384 tokens
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+ - **Output Dimensionality:** 768 tokens
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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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
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+ (1): Pooling({'word_embedding_dimension': 768, '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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+ (2): Normalize()
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+ )
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+ ```
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+
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+ ## Usage
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+
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+ ### Direct Usage (Sentence Transformers)
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+
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+ First install the Sentence Transformers library:
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+
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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("milnico/Personality_Cross_Encoder")
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+ # Run inference
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+ sentences = [
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+ 'Let others make the decisions.',
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+ 'Begin to panic when there is danger.',
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+ 'Must try to maintain harmony within my group.',
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+ ]
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+ embeddings = model.encode(sentences)
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+ print(embeddings.shape)
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+ # [3, 768]
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+
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+ # Get the similarity scores for the embeddings
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+ 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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+
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+ <details><summary>Click to see the direct usage in Transformers</summary>
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+
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+ </details>
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+ -->
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+
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+ <!--
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+ ### Downstream Usage (Sentence Transformers)
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+
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+ You can finetune this model on your own dataset.
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+
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+ <details><summary>Click to expand</summary>
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+
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+ </details>
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+ -->
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+
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+ <!--
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+ ### Out-of-Scope Use
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+
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+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+ -->
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+
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+ <!--
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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: 100,000 training samples
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+ * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | sentence1 | sentence2 | score |
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+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
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+ | type | string | string | float |
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+ | details | <ul><li>min: 5 tokens</li><li>mean: 8.39 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.77 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.11</li><li>max: 1.0</li></ul> |
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+ * Samples:
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+ | sentence1 | sentence2 | score |
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+ |:------------------------------------------------------------------|:-------------------------------------------------|:--------------------------|
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+ | <code>Don't worry about things that have already happened.</code> | <code>Dislike being complimented.</code> | <code>0.0046042455</code> |
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+ | <code>Follow directions.</code> | <code>Need things explained only once.</code> | <code>0.1702887</code> |
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+ | <code>Watched a television reality show.</code> | <code>Do more than what's expected of me.</code> | <code>0.12572353</code> |
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+ * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
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+ ```json
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+ {
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+ "loss_fct": "torch.nn.modules.loss.MSELoss"
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+ }
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+ ```
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+
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+ ### Evaluation Dataset
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+
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+ #### Unnamed Dataset
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+
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+
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+ * Size: 10,000 evaluation samples
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+ * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | sentence1 | sentence2 | score |
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+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
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+ | type | string | string | float |
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+ | details | <ul><li>min: 4 tokens</li><li>mean: 8.43 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.72 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.11</li><li>max: 1.0</li></ul> |
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+ * Samples:
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+ | sentence1 | sentence2 | score |
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+ |:-----------------------------------------|:-------------------------------------------------|:------------------------|
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+ | <code>Feel short-changed in life.</code> | <code>Never spend more than I can afford.</code> | <code>0.13934776</code> |
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+ | <code>Enjoy the beauty of nature.</code> | <code>Do things that others find strange.</code> | <code>0.065138</code> |
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+ | <code>Seldom get mad.</code> | <code>Make a decision and move on.</code> | <code>0.08558667</code> |
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+ * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
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+ ```json
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+ {
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+ "loss_fct": "torch.nn.modules.loss.MSELoss"
191
+ }
192
+ ```
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+
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+ ### Training Hyperparameters
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+ #### Non-Default Hyperparameters
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+
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+ - `eval_strategy`: steps
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+ - `per_device_train_batch_size`: 64
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+ - `per_device_eval_batch_size`: 64
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+ - `learning_rate`: 2e-05
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+ - `num_train_epochs`: 10
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+ - `warmup_ratio`: 0.1
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+ - `fp16`: True
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+ - `batch_sampler`: no_duplicates
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+
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+ #### All Hyperparameters
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+ <details><summary>Click to expand</summary>
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+
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+ - `overwrite_output_dir`: False
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+ - `do_predict`: False
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+ - `eval_strategy`: steps
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+ - `prediction_loss_only`: True
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+ - `per_device_train_batch_size`: 64
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+ - `per_device_eval_batch_size`: 64
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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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+ - `learning_rate`: 2e-05
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+ - `weight_decay`: 0.0
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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`: 10
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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`: 0
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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`: False
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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
277
+ - `group_by_length`: False
278
+ - `length_column_name`: length
279
+ - `ddp_find_unused_parameters`: None
280
+ - `ddp_bucket_cap_mb`: None
281
+ - `ddp_broadcast_buffers`: False
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+ - `dataloader_pin_memory`: True
283
+ - `dataloader_persistent_workers`: False
284
+ - `skip_memory_metrics`: True
285
+ - `use_legacy_prediction_loop`: False
286
+ - `push_to_hub`: False
287
+ - `resume_from_checkpoint`: None
288
+ - `hub_model_id`: None
289
+ - `hub_strategy`: every_save
290
+ - `hub_private_repo`: False
291
+ - `hub_always_push`: False
292
+ - `gradient_checkpointing`: False
293
+ - `gradient_checkpointing_kwargs`: None
294
+ - `include_inputs_for_metrics`: False
295
+ - `eval_do_concat_batches`: True
296
+ - `fp16_backend`: auto
297
+ - `push_to_hub_model_id`: None
298
+ - `push_to_hub_organization`: None
299
+ - `mp_parameters`:
300
+ - `auto_find_batch_size`: False
301
+ - `full_determinism`: False
302
+ - `torchdynamo`: None
303
+ - `ray_scope`: last
304
+ - `ddp_timeout`: 1800
305
+ - `torch_compile`: False
306
+ - `torch_compile_backend`: None
307
+ - `torch_compile_mode`: None
308
+ - `dispatch_batches`: None
309
+ - `split_batches`: None
310
+ - `include_tokens_per_second`: False
311
+ - `include_num_input_tokens_seen`: False
312
+ - `neftune_noise_alpha`: None
313
+ - `optim_target_modules`: None
314
+ - `batch_eval_metrics`: False
315
+ - `eval_on_start`: False
316
+ - `batch_sampler`: no_duplicates
317
+ - `multi_dataset_batch_sampler`: proportional
318
+
319
+ </details>
320
+
321
+ ### Training Logs
322
+ | Epoch | Step | Training Loss | loss |
323
+ |:------:|:-----:|:-------------:|:------:|
324
+ | 0.6398 | 1000 | 0.0074 | 0.0050 |
325
+ | 1.2783 | 2000 | 0.0046 | 0.0042 |
326
+ | 1.9181 | 3000 | 0.0036 | 0.0038 |
327
+ | 2.5566 | 4000 | 0.0031 | 0.0036 |
328
+ | 3.1951 | 5000 | 0.0026 | 0.0035 |
329
+ | 3.8349 | 6000 | 0.0022 | 0.0035 |
330
+ | 4.4734 | 7000 | 0.0022 | 0.0034 |
331
+ | 5.1120 | 8000 | 0.0019 | 0.0034 |
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+ | 5.7518 | 9000 | 0.0017 | 0.0033 |
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+ | 6.3903 | 10000 | 0.0016 | 0.0033 |
334
+ | 7.0288 | 11000 | 0.0015 | 0.0033 |
335
+ | 7.6686 | 12000 | 0.0014 | 0.0032 |
336
+ | 8.3071 | 13000 | 0.0013 | 0.0032 |
337
+ | 8.9469 | 14000 | 0.0012 | 0.0031 |
338
+ | 9.5854 | 15000 | 0.0012 | 0.0031 |
339
+
340
+
341
+ ### Framework Versions
342
+ - Python: 3.9.19
343
+ - Sentence Transformers: 3.0.1
344
+ - Transformers: 4.42.4
345
+ - PyTorch: 2.3.0+cu121
346
+ - Accelerate: 0.32.1
347
+ - Datasets: 2.20.0
348
+ - Tokenizers: 0.19.1
349
+
350
+ ## Citation
351
+
352
+ ### BibTeX
353
+
354
+ #### Sentence Transformers
355
+ ```bibtex
356
+ @inproceedings{reimers-2019-sentence-bert,
357
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
358
+ author = "Reimers, Nils and Gurevych, Iryna",
359
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
360
+ month = "11",
361
+ year = "2019",
362
+ publisher = "Association for Computational Linguistics",
363
+ url = "https://arxiv.org/abs/1908.10084",
364
+ }
365
+ ```
366
+
367
+ <!--
368
+ ## Glossary
369
+
370
+ *Clearly define terms in order to be accessible across audiences.*
371
+ -->
372
+
373
+ <!--
374
+ ## Model Card Authors
375
+
376
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
377
+ -->
378
+
379
+ <!--
380
+ ## Model Card Contact
381
+
382
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
383
+ -->
config.json ADDED
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+ {
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+ "_name_or_path": "./models/IPIP_BS64/final",
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+ "architectures": [
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+ "MPNetModel"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "bos_token_id": 0,
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+ "eos_token_id": 2,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 768,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 3072,
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+ "layer_norm_eps": 1e-05,
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+ "max_position_embeddings": 514,
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+ "model_type": "mpnet",
17
+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
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+ "pad_token_id": 1,
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+ "relative_attention_num_buckets": 32,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.42.4",
23
+ "vocab_size": 30527
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+ }
config_sentence_transformers.json ADDED
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+ {
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+ "__version__": {
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+ "sentence_transformers": "3.0.1",
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+ "transformers": "4.42.4",
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+ "pytorch": "2.3.0+cu121"
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+ },
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+ "prompts": {},
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+ "default_prompt_name": null,
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+ "similarity_fn_name": null
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+ }
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+ "name": "1",
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+ "path": "1_Pooling",
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+ "type": "sentence_transformers.models.Pooling"
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+ },
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+ "idx": 2,
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+ "name": "2",
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+ "path": "2_Normalize",
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+ "type": "sentence_transformers.models.Normalize"
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+ }
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+ ]
sentence_bert_config.json ADDED
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+ {
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+ "max_seq_length": 384,
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+ "do_lower_case": false
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+ }
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33
+ "normalized": false,
34
+ "rstrip": false,
35
+ "single_word": false
36
+ },
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+ "sep_token": {
38
+ "content": "</s>",
39
+ "lstrip": false,
40
+ "normalized": false,
41
+ "rstrip": false,
42
+ "single_word": false
43
+ },
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+ "unk_token": {
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+ "content": "[UNK]",
46
+ "lstrip": false,
47
+ "normalized": false,
48
+ "rstrip": false,
49
+ "single_word": false
50
+ }
51
+ }
tokenizer.json ADDED
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tokenizer_config.json ADDED
@@ -0,0 +1,72 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "0": {
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+ "content": "<s>",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "1": {
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+ "content": "<pad>",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "2": {
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+ "content": "</s>",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "3": {
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+ "content": "<unk>",
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+ "lstrip": false,
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+ "normalized": true,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "104": {
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+ "content": "[UNK]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "30526": {
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+ "content": "<mask>",
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+ "lstrip": true,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ }
51
+ },
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+ "bos_token": "<s>",
53
+ "clean_up_tokenization_spaces": true,
54
+ "cls_token": "<s>",
55
+ "do_lower_case": true,
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+ "eos_token": "</s>",
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+ "mask_token": "<mask>",
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+ "max_length": 128,
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+ "model_max_length": 384,
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+ "pad_to_multiple_of": null,
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+ "pad_token": "<pad>",
62
+ "pad_token_type_id": 0,
63
+ "padding_side": "right",
64
+ "sep_token": "</s>",
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+ "stride": 0,
66
+ "strip_accents": null,
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+ "tokenize_chinese_chars": true,
68
+ "tokenizer_class": "MPNetTokenizer",
69
+ "truncation_side": "right",
70
+ "truncation_strategy": "longest_first",
71
+ "unk_token": "[UNK]"
72
+ }
vocab.txt ADDED
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