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checkpoint-23000/1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 384,
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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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+ }
checkpoint-23000/README.md ADDED
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+ ---
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+ base_model: sentence-transformers/all-MiniLM-L6-v2
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+ datasets:
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+ - youssefkhalil320/pairs_three_scores_v5
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+ language:
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+ - en
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+ library_name: sentence-transformers
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+ license: apache-2.0
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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:80000003
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+ - loss:CoSENTLoss
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+ widget:
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+ - source_sentence: durable pvc swim ring
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+ sentences:
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+ - flaky croissant
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+ - urban shoes
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+ - warm drinks mug
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+ - source_sentence: iso mak retard capsules
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+ sentences:
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+ - savory baguette
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+ - shea butter body cream
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+ - softwheeled cruiser
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+ - source_sentence: love sandra potty
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+ sentences:
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+ - utensil holder
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+ - olive pants
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+ - headwear
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+ - source_sentence: dusky hair brush
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+ sentences:
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+ - back compartment laptop
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+ - rubber feet platter
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+ - honed blade knife
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+ - source_sentence: nkd skn
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+ sentences:
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+ - fruit fragrances nail polish remover
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+ - panini salmon
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+ - hand drawing bag
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+ ---
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+
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+ # all-MiniLM-L6-v8-pair_score
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the [pairs_three_scores_v5](https://huggingface.co/datasets/youssefkhalil320/pairs_three_scores_v5) dataset. It maps sentences & paragraphs to a 384-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-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
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+ - **Maximum Sequence Length:** 256 tokens
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+ - **Output Dimensionality:** 384 tokens
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+ - **Similarity Function:** Cosine Similarity
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+ - **Training Dataset:**
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+ - [pairs_three_scores_v5](https://huggingface.co/datasets/youssefkhalil320/pairs_three_scores_v5)
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+ - **Language:** en
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+ - **License:** apache-2.0
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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': 256, 'do_lower_case': False}) with Transformer model: BertModel
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+ (1): Pooling({'word_embedding_dimension': 384, '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("sentence_transformers_model_id")
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+ # Run inference
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+ sentences = [
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+ 'nkd skn',
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+ 'hand drawing bag',
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+ 'panini salmon',
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+ ]
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+ embeddings = model.encode(sentences)
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+ print(embeddings.shape)
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+ # [3, 384]
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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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+
146
+ ## Training Details
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+
148
+ ### Training Dataset
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+
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+ #### pairs_three_scores_v5
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+
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+ * Dataset: [pairs_three_scores_v5](https://huggingface.co/datasets/youssefkhalil320/pairs_three_scores_v5) at [3d8c457](https://huggingface.co/datasets/youssefkhalil320/pairs_three_scores_v5/tree/3d8c45703846bd2adfaaf422abafbc389b283de1)
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+ * Size: 80,000,003 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: 3 tokens</li><li>mean: 6.06 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.71 tokens</li><li>max: 13 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>vanilla hair cream</code> | <code>free of paraben hair mask</code> | <code>0.5</code> |
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+ | <code>nourishing shampoo</code> | <code>cumin lemon tea</code> | <code>0.0</code> |
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+ | <code>safe materials pacifier</code> | <code>facial serum</code> | <code>0.5</code> |
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+ * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) 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": "pairwise_cos_sim"
171
+ }
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+ ```
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+
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+ ### Evaluation Dataset
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+
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+ #### pairs_three_scores_v5
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+
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+ * Dataset: [pairs_three_scores_v5](https://huggingface.co/datasets/youssefkhalil320/pairs_three_scores_v5) at [3d8c457](https://huggingface.co/datasets/youssefkhalil320/pairs_three_scores_v5/tree/3d8c45703846bd2adfaaf422abafbc389b283de1)
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+ * Size: 20,000,001 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: 3 tokens</li><li>mean: 6.21 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.75 tokens</li><li>max: 12 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>teddy bear toy</code> | <code>long lasting cat food</code> | <code>0.0</code> |
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+ | <code>eva hair treatment</code> | <code>fresh pineapple</code> | <code>0.0</code> |
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+ | <code>soft wave hair conditioner</code> | <code>hybrid seat bike</code> | <code>0.0</code> |
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+ * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) 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": "pairwise_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`: 128
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+ - `per_device_eval_batch_size`: 128
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+ - `learning_rate`: 2e-05
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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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+
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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`: 128
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+ - `per_device_eval_batch_size`: 128
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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`: 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`: 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
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+ - `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
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+ - `include_inputs_for_metrics`: False
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+ - `eval_do_concat_batches`: True
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+ - `fp16_backend`: auto
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+ - `push_to_hub_model_id`: None
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+ - `push_to_hub_organization`: None
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+ - `mp_parameters`:
306
+ - `auto_find_batch_size`: False
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+ - `full_determinism`: False
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+ - `torchdynamo`: None
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+ - `ray_scope`: last
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+ - `ddp_timeout`: 1800
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+ - `torch_compile`: False
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+ - `torch_compile_backend`: None
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+ - `torch_compile_mode`: None
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+ - `dispatch_batches`: None
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+ - `split_batches`: None
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+ - `include_tokens_per_second`: False
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+ - `include_num_input_tokens_seen`: False
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+ - `neftune_noise_alpha`: None
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+ - `optim_target_modules`: None
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+ - `batch_eval_metrics`: False
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+ - `eval_on_start`: False
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+ - `use_liger_kernel`: False
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+ - `eval_use_gather_object`: False
324
+ - `batch_sampler`: batch_sampler
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+ - `multi_dataset_batch_sampler`: proportional
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+
327
+ </details>
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+
329
+ ### Training Logs
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+ <details><summary>Click to expand</summary>
331
+
332
+ | Epoch | Step | Training Loss |
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+ |:------:|:-----:|:-------------:|
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+ | 0.0002 | 100 | 10.8792 |
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+ | 0.0003 | 200 | 10.9284 |
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+ | 0.0005 | 300 | 10.6466 |
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+ | 0.0006 | 400 | 10.841 |
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+ | 0.0008 | 500 | 10.8094 |
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+ | 0.0010 | 600 | 10.4323 |
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+ | 0.0011 | 700 | 10.3032 |
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+ | 0.0013 | 800 | 10.4006 |
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+ | 0.0014 | 900 | 10.4743 |
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+ | 0.0016 | 1000 | 10.2334 |
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+ | 0.0018 | 1100 | 10.0135 |
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+ | 0.0019 | 1200 | 9.7874 |
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+ | 0.0021 | 1300 | 9.7419 |
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+ | 0.0022 | 1400 | 9.7412 |
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+ | 0.0024 | 1500 | 9.4585 |
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+ | 0.0026 | 1600 | 9.5339 |
350
+ | 0.0027 | 1700 | 9.4345 |
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+ | 0.0029 | 1800 | 9.1733 |
352
+ | 0.0030 | 1900 | 8.9952 |
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+ | 0.0032 | 2000 | 8.9669 |
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+ | 0.0034 | 2100 | 8.8152 |
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+ | 0.0035 | 2200 | 8.7936 |
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+ | 0.0037 | 2300 | 8.6771 |
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+ | 0.0038 | 2400 | 8.4648 |
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+ | 0.0040 | 2500 | 8.5764 |
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+ | 0.0042 | 2600 | 8.4587 |
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+ | 0.0043 | 2700 | 8.2966 |
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+ | 0.0045 | 2800 | 8.2329 |
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+ | 0.0046 | 2900 | 8.1415 |
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+ | 0.0048 | 3000 | 8.0404 |
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+ | 0.0050 | 3100 | 7.9698 |
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+ | 0.0051 | 3200 | 7.9205 |
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+ | 0.0053 | 3300 | 7.8314 |
367
+ | 0.0054 | 3400 | 7.8369 |
368
+ | 0.0056 | 3500 | 7.6403 |
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+ | 0.0058 | 3600 | 7.5842 |
370
+ | 0.0059 | 3700 | 7.5812 |
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+ | 0.0061 | 3800 | 7.4335 |
372
+ | 0.0062 | 3900 | 7.4917 |
373
+ | 0.0064 | 4000 | 7.3204 |
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+ | 0.0066 | 4100 | 7.2971 |
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+ | 0.0067 | 4200 | 7.2233 |
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+ | 0.0069 | 4300 | 7.2081 |
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+ | 0.0070 | 4400 | 7.1364 |
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+ | 0.0072 | 4500 | 7.0663 |
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+ | 0.0074 | 4600 | 6.9601 |
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+ | 0.0075 | 4700 | 6.9546 |
381
+ | 0.0077 | 4800 | 6.9019 |
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+ | 0.0078 | 4900 | 6.8801 |
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+ | 0.0080 | 5000 | 6.7734 |
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+ | 0.0082 | 5100 | 6.7648 |
385
+ | 0.0083 | 5200 | 6.7498 |
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+ | 0.0085 | 5300 | 6.6872 |
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+ | 0.0086 | 5400 | 6.6264 |
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+ | 0.0088 | 5500 | 6.579 |
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+ | 0.0090 | 5600 | 6.6001 |
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+ | 0.0091 | 5700 | 6.5971 |
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+ | 0.0093 | 5800 | 6.4694 |
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+ | 0.0094 | 5900 | 6.3983 |
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+ | 0.0096 | 6000 | 6.4477 |
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+ | 0.0098 | 6100 | 6.4308 |
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+ | 0.0099 | 6200 | 6.4248 |
396
+ | 0.0101 | 6300 | 6.2642 |
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+ | 0.0102 | 6400 | 6.2763 |
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+ | 0.0104 | 6500 | 6.3878 |
399
+ | 0.0106 | 6600 | 6.2601 |
400
+ | 0.0107 | 6700 | 6.1789 |
401
+ | 0.0109 | 6800 | 6.1773 |
402
+ | 0.0110 | 6900 | 6.1439 |
403
+ | 0.0112 | 7000 | 6.1863 |
404
+ | 0.0114 | 7100 | 6.0513 |
405
+ | 0.0115 | 7200 | 6.0671 |
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+ | 0.0117 | 7300 | 6.0212 |
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+ | 0.0118 | 7400 | 6.0043 |
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+ | 0.0120 | 7500 | 6.0166 |
409
+ | 0.0122 | 7600 | 5.9754 |
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+ | 0.0123 | 7700 | 5.9211 |
411
+ | 0.0125 | 7800 | 5.7867 |
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+ | 0.0126 | 7900 | 5.8534 |
413
+ | 0.0128 | 8000 | 5.7708 |
414
+ | 0.0130 | 8100 | 5.8328 |
415
+ | 0.0131 | 8200 | 5.7417 |
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+ | 0.0133 | 8300 | 5.8097 |
417
+ | 0.0134 | 8400 | 5.7578 |
418
+ | 0.0136 | 8500 | 5.643 |
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+ | 0.0138 | 8600 | 5.6401 |
420
+ | 0.0139 | 8700 | 5.6627 |
421
+ | 0.0141 | 8800 | 5.6167 |
422
+ | 0.0142 | 8900 | 5.6539 |
423
+ | 0.0144 | 9000 | 5.4513 |
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+ | 0.0146 | 9100 | 5.4132 |
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+ | 0.0147 | 9200 | 5.4714 |
426
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+ | 0.0150 | 9400 | 5.3928 |
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+ | 0.0152 | 9500 | 5.4774 |
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+ | 0.0154 | 9600 | 5.2881 |
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+ | 0.0155 | 9700 | 5.3699 |
431
+ | 0.0157 | 9800 | 5.1483 |
432
+ | 0.0158 | 9900 | 5.3051 |
433
+ | 0.0160 | 10000 | 5.2546 |
434
+ | 0.0162 | 10100 | 5.2314 |
435
+ | 0.0163 | 10200 | 5.1783 |
436
+ | 0.0165 | 10300 | 5.2074 |
437
+ | 0.0166 | 10400 | 5.2825 |
438
+ | 0.0168 | 10500 | 5.1715 |
439
+ | 0.0170 | 10600 | 5.087 |
440
+ | 0.0171 | 10700 | 5.082 |
441
+ | 0.0173 | 10800 | 4.9111 |
442
+ | 0.0174 | 10900 | 5.0213 |
443
+ | 0.0176 | 11000 | 4.9898 |
444
+ | 0.0178 | 11100 | 4.7734 |
445
+ | 0.0179 | 11200 | 4.9511 |
446
+ | 0.0181 | 11300 | 5.0481 |
447
+ | 0.0182 | 11400 | 4.8441 |
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+ | 0.0184 | 11500 | 4.873 |
449
+ | 0.0186 | 11600 | 4.9988 |
450
+ | 0.0187 | 11700 | 4.7653 |
451
+ | 0.0189 | 11800 | 4.804 |
452
+ | 0.0190 | 11900 | 4.8288 |
453
+ | 0.0192 | 12000 | 4.7053 |
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+ | 0.0194 | 12100 | 4.6887 |
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456
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+ | 0.0200 | 12500 | 4.5936 |
459
+ | 0.0202 | 12600 | 4.7452 |
460
+ | 0.0203 | 12700 | 4.5321 |
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462
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463
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536
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537
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538
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541
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542
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543
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544
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545
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546
+ | 0.0341 | 21300 | 3.0967 |
547
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548
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549
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550
+ | 0.0347 | 21700 | 2.4912 |
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+ | 0.0349 | 21800 | 2.5079 |
552
+ | 0.0350 | 21900 | 3.2942 |
553
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554
+ | 0.0354 | 22100 | 2.782 |
555
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556
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557
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558
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559
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560
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561
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562
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563
+ | 0.0368 | 23000 | 2.5376 |
564
+
565
+ </details>
566
+
567
+ ### Framework Versions
568
+ - Python: 3.8.10
569
+ - Sentence Transformers: 3.1.1
570
+ - Transformers: 4.45.2
571
+ - PyTorch: 2.4.1+cu118
572
+ - Accelerate: 1.0.1
573
+ - Datasets: 3.0.1
574
+ - Tokenizers: 0.20.3
575
+
576
+ ## Citation
577
+
578
+ ### BibTeX
579
+
580
+ #### Sentence Transformers
581
+ ```bibtex
582
+ @inproceedings{reimers-2019-sentence-bert,
583
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
584
+ author = "Reimers, Nils and Gurevych, Iryna",
585
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
586
+ month = "11",
587
+ year = "2019",
588
+ publisher = "Association for Computational Linguistics",
589
+ url = "https://arxiv.org/abs/1908.10084",
590
+ }
591
+ ```
592
+
593
+ #### CoSENTLoss
594
+ ```bibtex
595
+ @online{kexuefm-8847,
596
+ title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
597
+ author={Su Jianlin},
598
+ year={2022},
599
+ month={Jan},
600
+ url={https://kexue.fm/archives/8847},
601
+ }
602
+ ```
603
+
604
+ <!--
605
+ ## Glossary
606
+
607
+ *Clearly define terms in order to be accessible across audiences.*
608
+ -->
609
+
610
+ <!--
611
+ ## Model Card Authors
612
+
613
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
614
+ -->
615
+
616
+ <!--
617
+ ## Model Card Contact
618
+
619
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
620
+ -->
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checkpoint-23000/vocab.txt ADDED
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