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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:2400
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+ - loss:MultipleNegativesRankingLoss
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+ widget:
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+ - source_sentence: Are there any furniture stores? (variation 536)
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+ sentences:
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+ - Event tickets can be purchased at the customer service desk or online through
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+ the mall's website.
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+ - The Apple Store is located on the second floor near the food court.
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+ - Yes, there are furniture stores including IKEA and Ashley Furniture, both located
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+ on the second floor.
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+ - source_sentence: Is there a play area for kids? (variation 121)
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+ sentences:
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+ - The customer service desk is located on the ground floor near the main entrance.
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+ - Yes, there is a play area for kids on the first floor near the west entrance.
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+ - Yes, there is a luggage store on the second floor near the central atrium.
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+ - source_sentence: Are there any sports stores? (variation 931)
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+ sentences:
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+ - Yes, there is a toy store on the first floor near the west entrance.
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+ - Event tickets can be purchased at the customer service desk or online through
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+ the mall's website.
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+ - Yes, there are sports stores including Nike and Adidas, both located on the first
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+ floor.
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+ - source_sentence: Where can I charge my phone? (variation 904)
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+ sentences:
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+ - Yes, reservations for 'The Gourmet Palace' can be made by calling their direct
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+ line or via their website.
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+ - Yes, there is a photography studio on the first floor near the main entrance.
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+ - Phone charging stations are available throughout the mall, including near the
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+ food court and at the customer service desk.
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+ - source_sentence: Does the mall have a post office? (variation 1412)
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+ sentences:
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+ - Yes, there is a photography studio on the first floor near the main entrance.
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+ - Yes, there is a game arcade on the third floor next to the cinema.
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+ - Yes, there is a post office on the ground floor near the west entrance.
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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) on the train dataset. 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:**
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+ - train
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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("anomys/gsm-finetunned")
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+ # Run inference
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+ sentences = [
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+ 'Does the mall have a post office? (variation 1412)',
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+ 'Yes, there is a post office on the ground floor near the west entrance.',
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+ 'Yes, there is a game arcade on the third floor next to the cinema.',
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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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+ #### train
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+
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+ * Dataset: train
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+ * Size: 2,400 training samples
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+ * Columns: <code>question</code> and <code>response</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | question | response |
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+ |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
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+ | type | string | string |
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+ | details | <ul><li>min: 12 tokens</li><li>mean: 15.28 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 21.73 tokens</li><li>max: 33 tokens</li></ul> |
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+ * Samples:
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+ | question | response |
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+ |:-------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------|
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+ | <code>Where can I find an ATM in the mall? (variation 643)</code> | <code>ATMs are located on the ground floor next to the information desk and near the west entrance.</code> |
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+ | <code>Is there a map of the mall available? (variation 701)</code> | <code>Yes, you can find interactive maps on our website and physical maps at the information desks located at each entrance.</code> |
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+ | <code>Where can I find the customer service desk? (variation 227)</code> | <code>The customer service desk is located on the ground floor near the main entrance.</code> |
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+ * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
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+ ```json
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+ {
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+ "scale": 20.0,
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+ "similarity_fct": "cos_sim"
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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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+ #### train
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+
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+ * Dataset: train
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+ * Size: 600 evaluation samples
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+ * Columns: <code>question</code> and <code>response</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | question | response |
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+ |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
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+ | type | string | string |
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+ | details | <ul><li>min: 12 tokens</li><li>mean: 15.22 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 21.35 tokens</li><li>max: 33 tokens</li></ul> |
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+ * Samples:
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+ | question | response |
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+ |:-----------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------|
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+ | <code>Are there any opticians in the mall? (variation 1802)</code> | <code>Yes, there are opticians including LensCrafters and Visionworks, both located on the first floor.</code> |
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+ | <code>Is there a map of the mall available? (variation 1191)</code> | <code>Yes, you can find interactive maps on our website and physical maps at the information desks located at each entrance.</code> |
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+ | <code>Are there any wheelchair-accessible entrances? (variation 1818)</code> | <code>Yes, all main entrances are wheelchair accessible, and we provide complimentary wheelchair rentals at the customer service desk.</code> |
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+ * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
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+ ```json
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+ {
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+ "scale": 20.0,
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+ "similarity_fct": "cos_sim"
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+ }
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+ ```
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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`: 16
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+ - `per_device_eval_batch_size`: 16
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+ - `num_train_epochs`: 1
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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`: 16
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+ - `per_device_eval_batch_size`: 16
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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`: 5e-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`: 1
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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
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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
296
+ - `hub_model_id`: None
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+ - `hub_strategy`: every_save
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+ - `hub_private_repo`: False
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+ - `hub_always_push`: False
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+ - `gradient_checkpointing`: False
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+ - `gradient_checkpointing_kwargs`: None
302
+ - `include_inputs_for_metrics`: False
303
+ - `eval_do_concat_batches`: True
304
+ - `fp16_backend`: auto
305
+ - `push_to_hub_model_id`: None
306
+ - `push_to_hub_organization`: None
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+ - `mp_parameters`:
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+ - `auto_find_batch_size`: False
309
+ - `full_determinism`: False
310
+ - `torchdynamo`: None
311
+ - `ray_scope`: last
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+ - `ddp_timeout`: 1800
313
+ - `torch_compile`: False
314
+ - `torch_compile_backend`: None
315
+ - `torch_compile_mode`: None
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+ - `dispatch_batches`: None
317
+ - `split_batches`: None
318
+ - `include_tokens_per_second`: False
319
+ - `include_num_input_tokens_seen`: False
320
+ - `neftune_noise_alpha`: None
321
+ - `optim_target_modules`: None
322
+ - `batch_eval_metrics`: False
323
+ - `batch_sampler`: no_duplicates
324
+ - `multi_dataset_batch_sampler`: proportional
325
+
326
+ </details>
327
+
328
+ ### Training Logs
329
+ | Epoch | Step | Training Loss | train loss |
330
+ |:------:|:----:|:-------------:|:----------:|
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+ | 0.3333 | 50 | 0.0083 | 0.0000 |
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+ | 0.6667 | 100 | 0.0 | 0.0000 |
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+ | 1.0 | 150 | 0.0 | 0.0000 |
334
+
335
+
336
+ ### Framework Versions
337
+ - Python: 3.10.12
338
+ - Sentence Transformers: 3.0.1
339
+ - Transformers: 4.41.2
340
+ - PyTorch: 2.3.0+cu121
341
+ - Accelerate: 0.32.1
342
+ - Datasets: 2.20.0
343
+ - Tokenizers: 0.19.1
344
+
345
+ ## Citation
346
+
347
+ ### BibTeX
348
+
349
+ #### Sentence Transformers
350
+ ```bibtex
351
+ @inproceedings{reimers-2019-sentence-bert,
352
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
353
+ author = "Reimers, Nils and Gurevych, Iryna",
354
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
355
+ month = "11",
356
+ year = "2019",
357
+ publisher = "Association for Computational Linguistics",
358
+ url = "https://arxiv.org/abs/1908.10084",
359
+ }
360
+ ```
361
+
362
+ #### MultipleNegativesRankingLoss
363
+ ```bibtex
364
+ @misc{henderson2017efficient,
365
+ title={Efficient Natural Language Response Suggestion for Smart Reply},
366
+ author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
367
+ year={2017},
368
+ eprint={1705.00652},
369
+ archivePrefix={arXiv},
370
+ primaryClass={cs.CL}
371
+ }
372
+ ```
373
+
374
+ <!--
375
+ ## Glossary
376
+
377
+ *Clearly define terms in order to be accessible across audiences.*
378
+ -->
379
+
380
+ <!--
381
+ ## Model Card Authors
382
+
383
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
384
+ -->
385
+
386
+ <!--
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+ ## Model Card Contact
388
+
389
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
config.json ADDED
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+ {
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+ "_name_or_path": "sentence-transformers/all-mpnet-base-v2",
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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",
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+ "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": "4.41.2",
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+ "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": "4.41.2",
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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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+ size 437967672
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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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+ {
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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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+ "content": "<mask>",
25
+ "lstrip": true,
26
+ "normalized": false,
27
+ "rstrip": false,
28
+ "single_word": false
29
+ },
30
+ "pad_token": {
31
+ "content": "<pad>",
32
+ "lstrip": false,
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>",
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+ "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",
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+ "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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