pjbhaumik commited on
Commit
23faa01
1 Parent(s): 646c081

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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+ language: []
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+ library_name: sentence-transformers
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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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+ - dataset_size:10K<n<100K
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+ - loss:MultipleNegativesSymmetricRankingLoss
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+ base_model: distilbert/distilbert-base-uncased
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+ metrics:
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+ - pearson_cosine
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+ - spearman_cosine
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+ - pearson_manhattan
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+ - spearman_manhattan
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+ - pearson_euclidean
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+ - spearman_euclidean
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+ - pearson_dot
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+ - spearman_dot
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+ - pearson_max
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+ - spearman_max
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+ widget:
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+ - source_sentence: what is GOGO
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+ sentences:
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+ - What is Viasat
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+ - are we flying into Tel Aviv
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+ - how do i correct a name in term
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+ - source_sentence: What is EU 261
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+ sentences:
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+ - is puj a EU compensation country
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+ - can i take my bicycle on af
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+ - flight delays over 6 hours
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+ - source_sentence: Can i get wifi
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+ sentences:
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+ - which aircrafts do not have wifi
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+ - military traveling with pet
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+ - baggage delay to carousel
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+ - source_sentence: austin airport
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+ sentences:
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+ - What time is IAH open
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+ - amex card free checked bag
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+ - what is upgrade companion
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+ - source_sentence: pets in cargo
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+ sentences:
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+ - can a pet travel in cargo
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+ - baggage exceptions for Amex
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+ - how do I get sky priority
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+ pipeline_tag: sentence-similarity
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+ model-index:
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+ - name: SentenceTransformer based on distilbert/distilbert-base-uncased
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+ results:
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+ - task:
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+ type: semantic-similarity
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+ name: Semantic Similarity
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+ dataset:
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+ name: eval examples
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+ type: eval_examples
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+ metrics:
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+ - type: pearson_cosine
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+ value: .nan
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: .nan
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+ name: Spearman Cosine
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+ - type: pearson_manhattan
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+ value: .nan
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+ name: Pearson Manhattan
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+ - type: spearman_manhattan
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+ value: .nan
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+ name: Spearman Manhattan
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+ - type: pearson_euclidean
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+ value: .nan
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+ name: Pearson Euclidean
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+ - type: spearman_euclidean
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+ value: .nan
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+ name: Spearman Euclidean
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+ - type: pearson_dot
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+ value: .nan
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+ name: Pearson Dot
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+ - type: spearman_dot
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+ value: .nan
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+ name: Spearman Dot
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+ - type: pearson_max
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+ value: .nan
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+ name: Pearson Max
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+ - type: spearman_max
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+ value: .nan
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+ name: Spearman Max
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+ ---
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+
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+ # SentenceTransformer based on distilbert/distilbert-base-uncased
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased). 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:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) <!-- at revision 12040accade4e8a0f71eabdb258fecc2e7e948be -->
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+ - **Maximum Sequence Length:** 512 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': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel
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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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+ )
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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("pjbhaumik/biencoder-finetune-model-v9")
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+ # Run inference
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+ sentences = [
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+ 'pets in cargo',
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+ 'can a pet travel in cargo',
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+ 'baggage exceptions for Amex',
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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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+ ## Evaluation
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+
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+ ### Metrics
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+
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+ #### Semantic Similarity
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+ * Dataset: `eval_examples`
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+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
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+
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+ | Metric | Value |
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+ |:-------------------|:--------|
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+ | pearson_cosine | nan |
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+ | spearman_cosine | nan |
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+ | pearson_manhattan | nan |
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+ | spearman_manhattan | nan |
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+ | pearson_euclidean | nan |
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+ | spearman_euclidean | nan |
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+ | pearson_dot | nan |
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+ | spearman_dot | nan |
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+ | pearson_max | nan |
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+ | **spearman_max** | **nan** |
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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: 15,488 training samples
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+ * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | sentence_0 | sentence_1 | label |
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+ |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------|
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+ | type | string | string | int |
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+ | details | <ul><li>min: 4 tokens</li><li>mean: 10.4 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.14 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>1: 100.00%</li></ul> |
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+ * Samples:
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+ | sentence_0 | sentence_1 | label |
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+ |:-----------------------------------------------------------------|:-------------------------------------------------------------------------------------|:---------------|
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+ | <code>how to use a companion certificate on delta.com</code> | <code>SHOPPING ON DELTA.COM FOR AMEX CERT</code> | <code>1</code> |
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+ | <code>is jamaica can be booked with companion certificate</code> | <code>what areas can the American Express companion certificate be applied to</code> | <code>1</code> |
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+ | <code>how do i book award travel on klm</code> | <code>can you book an air france ticket with miles</code> | <code>1</code> |
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+ * Loss: [<code>MultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) 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`: 12
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+ - `multi_dataset_batch_sampler`: round_robin
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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
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+ - `num_train_epochs`: 12
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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`: 0.0
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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`: False
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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`:
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+ - `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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+ - `batch_sampler`: batch_sampler
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+ - `multi_dataset_batch_sampler`: round_robin
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+
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+ </details>
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+
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+ ### Training Logs
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+ <details><summary>Click to expand</summary>
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+
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+ | Epoch | Step | Training Loss | eval_examples_spearman_max |
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+ |:-------:|:-----:|:-------------:|:--------------------------:|
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+ | 0.1033 | 100 | - | nan |
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+ | 0.2066 | 200 | - | nan |
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+ | 0.3099 | 300 | - | nan |
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+ | 0.4132 | 400 | - | nan |
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+ | 0.5165 | 500 | 0.7655 | nan |
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+ | 0.6198 | 600 | - | nan |
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+ | 0.7231 | 700 | - | nan |
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+ | 0.8264 | 800 | - | nan |
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+ | 0.9298 | 900 | - | nan |
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+ | 1.0 | 968 | - | nan |
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+ | 1.0331 | 1000 | 0.3727 | nan |
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+ | 1.1364 | 1100 | - | nan |
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+ | 1.2397 | 1200 | - | nan |
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+ | 1.3430 | 1300 | - | nan |
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+ | 1.4463 | 1400 | - | nan |
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+ | 1.5496 | 1500 | 0.2686 | nan |
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+ | 1.6529 | 1600 | - | nan |
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+ | 1.7562 | 1700 | - | nan |
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+ | 1.8595 | 1800 | - | nan |
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+ | 1.9628 | 1900 | - | nan |
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+ | 2.0 | 1936 | - | nan |
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+ | 2.0661 | 2000 | 0.2709 | nan |
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+ | 2.1694 | 2100 | - | nan |
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+ | 2.2727 | 2200 | - | nan |
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+ | 2.3760 | 2300 | - | nan |
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+ | 2.4793 | 2400 | - | nan |
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+ | 2.5826 | 2500 | 0.231 | nan |
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+ | 2.6860 | 2600 | - | nan |
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+ | 2.7893 | 2700 | - | nan |
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+ | 2.8926 | 2800 | - | nan |
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+ | 2.9959 | 2900 | - | nan |
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+ | 3.0 | 2904 | - | nan |
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+ | 3.0992 | 3000 | 0.2461 | nan |
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+ | 3.2025 | 3100 | - | nan |
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+ | 3.3058 | 3200 | - | nan |
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+ | 3.4091 | 3300 | - | nan |
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+ | 3.5124 | 3400 | - | nan |
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+ | 3.6157 | 3500 | 0.2181 | nan |
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+ | 3.7190 | 3600 | - | nan |
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+ | 3.8223 | 3700 | - | nan |
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+ | 3.9256 | 3800 | - | nan |
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+ | 4.0 | 3872 | - | nan |
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+ | 4.0289 | 3900 | - | nan |
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+ | 4.1322 | 4000 | 0.2288 | nan |
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+ | 4.2355 | 4100 | - | nan |
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+ | 4.3388 | 4200 | - | nan |
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+ | 4.4421 | 4300 | - | nan |
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+ | 4.5455 | 4400 | - | nan |
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+ | 4.6488 | 4500 | 0.2123 | nan |
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+ | 4.7521 | 4600 | - | nan |
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+ | 4.8554 | 4700 | - | nan |
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+ | 4.9587 | 4800 | - | nan |
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+ | 5.0 | 4840 | - | nan |
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+ | 5.0620 | 4900 | - | nan |
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+ | 5.1653 | 5000 | 0.2254 | nan |
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+ | 5.2686 | 5100 | - | nan |
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+ | 5.3719 | 5200 | - | nan |
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+ | 5.4752 | 5300 | - | nan |
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+ | 5.5785 | 5400 | - | nan |
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+ | 5.6818 | 5500 | 0.2077 | nan |
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+ | 5.7851 | 5600 | - | nan |
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+ | 5.8884 | 5700 | - | nan |
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+ | 5.9917 | 5800 | - | nan |
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+ | 6.0 | 5808 | - | nan |
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+ | 6.0950 | 5900 | - | nan |
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+ | 6.1983 | 6000 | 0.218 | nan |
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+ | 6.3017 | 6100 | - | nan |
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+ | 6.4050 | 6200 | - | nan |
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+ | 6.5083 | 6300 | - | nan |
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+ | 6.6116 | 6400 | - | nan |
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+ | 6.7149 | 6500 | 0.206 | nan |
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+ | 6.8182 | 6600 | - | nan |
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+ | 6.9215 | 6700 | - | nan |
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+ | 7.0 | 6776 | - | nan |
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+ | 7.0248 | 6800 | - | nan |
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+ | 7.1281 | 6900 | - | nan |
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+ | 7.2314 | 7000 | 0.2126 | nan |
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+ | 7.3347 | 7100 | - | nan |
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+ | 7.4380 | 7200 | - | nan |
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+ | 7.5413 | 7300 | - | nan |
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+ | 7.6446 | 7400 | - | nan |
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+ | 7.7479 | 7500 | 0.2065 | nan |
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+ | 7.8512 | 7600 | - | nan |
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+ | 7.9545 | 7700 | - | nan |
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+ | 8.0 | 7744 | - | nan |
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+ | 8.0579 | 7800 | - | nan |
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+ | 8.1612 | 7900 | - | nan |
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+ | 8.2645 | 8000 | 0.2068 | nan |
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+ | 8.3678 | 8100 | - | nan |
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+ | 8.4711 | 8200 | - | nan |
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+ | 8.5744 | 8300 | - | nan |
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+ | 8.6777 | 8400 | - | nan |
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+ | 8.7810 | 8500 | 0.2014 | nan |
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+ | 8.8843 | 8600 | - | nan |
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+ | 8.9876 | 8700 | - | nan |
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+ | 9.0 | 8712 | - | nan |
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+ | 9.0909 | 8800 | - | nan |
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+ | 9.1942 | 8900 | - | nan |
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+ | 9.2975 | 9000 | 0.2057 | nan |
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+ | 9.4008 | 9100 | - | nan |
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+ | 9.5041 | 9200 | - | nan |
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+ | 9.6074 | 9300 | - | nan |
469
+ | 9.7107 | 9400 | - | nan |
470
+ | 9.8140 | 9500 | 0.1969 | nan |
471
+ | 9.9174 | 9600 | - | nan |
472
+ | 10.0 | 9680 | - | nan |
473
+ | 10.0207 | 9700 | - | nan |
474
+ | 10.1240 | 9800 | - | nan |
475
+ | 10.2273 | 9900 | - | nan |
476
+ | 10.3306 | 10000 | 0.2023 | nan |
477
+ | 10.4339 | 10100 | - | nan |
478
+ | 10.5372 | 10200 | - | nan |
479
+ | 10.6405 | 10300 | - | nan |
480
+ | 10.7438 | 10400 | - | nan |
481
+ | 10.8471 | 10500 | 0.1946 | nan |
482
+ | 10.9504 | 10600 | - | nan |
483
+ | 11.0 | 10648 | - | nan |
484
+ | 11.0537 | 10700 | - | nan |
485
+ | 11.1570 | 10800 | - | nan |
486
+ | 11.2603 | 10900 | - | nan |
487
+ | 11.3636 | 11000 | 0.1982 | nan |
488
+ | 11.4669 | 11100 | - | nan |
489
+ | 11.5702 | 11200 | - | nan |
490
+ | 11.6736 | 11300 | - | nan |
491
+ | 11.7769 | 11400 | - | nan |
492
+ | 11.8802 | 11500 | 0.1919 | nan |
493
+ | 11.9835 | 11600 | - | nan |
494
+ | 12.0 | 11616 | - | nan |
495
+
496
+ </details>
497
+
498
+ ### Framework Versions
499
+ - Python: 3.10.14
500
+ - Sentence Transformers: 3.0.0
501
+ - Transformers: 4.41.2
502
+ - PyTorch: 2.1.0
503
+ - Accelerate: 0.30.1
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+ - Datasets: 2.19.1
505
+ - Tokenizers: 0.19.1
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+
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+ ## Citation
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+
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+ ### BibTeX
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+
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+ #### Sentence Transformers
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+ ```bibtex
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+ @inproceedings{reimers-2019-sentence-bert,
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+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
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+ author = "Reimers, Nils and Gurevych, Iryna",
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+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
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+ month = "11",
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+ year = "2019",
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+ publisher = "Association for Computational Linguistics",
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+ url = "https://arxiv.org/abs/1908.10084",
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+ }
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+ ```
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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
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+
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+ <!--
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+ ## Model Card Contact
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+
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+ *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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+ -->
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