trbeers commited on
Commit
62e506d
1 Parent(s): e0d84b3

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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+ - generated_from_trainer
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+ - dataset_size:8137
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+ - loss:CosineSimilarityLoss
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+ base_model: distilbert/distilroberta-base
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+ datasets: []
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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: Proficient in chemical or plasma cleaning methods.
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+ sentences:
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+ - Skilled in circuit board assembly
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+ - Created custom reports in Workday for HR metrics
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+ - Developed a website using HTML and CSS
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+ - source_sentence: Expertise in data modeling, SQL query design, and execution, preferably
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+ in the financial services sector.
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+ sentences:
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+ - over 2 years of working in a retail customer support role
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+ - Operated a forklift for material handling
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+ - Proficient in crafting SQL queries for large datasets
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+ - source_sentence: The ability to collaborate across teams and adapt to a fast-paced
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+ environment is highly valued.
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+ sentences:
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+ - Demonstrated flexibility in meeting tight deadlines while working with cross-functional
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+ teams
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+ - Processed confidential client documents with high attention to detail
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+ - Assisted with quality control checks on finished products
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+ - source_sentence: Experience advocating for clients while effectively managing tough
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+ conversations.
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+ sentences:
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+ - Designed responsive web layouts with HTML and CSS
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+ - managed BIM coordination projects using Navisworks
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+ - Focused solely on administrative tasks without client involvement
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+ - source_sentence: Knowledge of medical equipment and veterinary terminology is necessary.
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+ sentences:
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+ - Conducted electrical system design reviews
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+ - Skilled in component sorting for various projects
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+ - Worked as a pet trainer for obedience classes
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+ pipeline_tag: sentence-similarity
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+ model-index:
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+ - name: SentenceTransformer based on distilbert/distilroberta-base
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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: sts test
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+ type: sts-test
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.8711224171717953
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.8269886257122767
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+ name: Spearman Cosine
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+ - type: pearson_manhattan
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+ value: 0.8510242443923921
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+ name: Pearson Manhattan
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+ - type: spearman_manhattan
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+ value: 0.8224876706713816
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+ name: Spearman Manhattan
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+ - type: pearson_euclidean
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+ value: 0.8563696604724638
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+ name: Pearson Euclidean
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+ - type: spearman_euclidean
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+ value: 0.8221599636921783
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+ name: Spearman Euclidean
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+ - type: pearson_dot
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+ value: 0.8482029844070074
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+ name: Pearson Dot
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+ - type: spearman_dot
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+ value: 0.8223271611305473
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+ name: Spearman Dot
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+ - type: pearson_max
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+ value: 0.8711224171717953
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+ name: Pearson Max
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+ - type: spearman_max
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+ value: 0.8269886257122767
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+ name: Spearman Max
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+ ---
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+
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+ # SentenceTransformer based on distilbert/distilroberta-base
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base). 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/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) <!-- at revision fb53ab8802853c8e4fbdbcd0529f21fc6f459b2b -->
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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: RobertaModel
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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("trbeers/distilroberta-base-sts")
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+ # Run inference
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+ sentences = [
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+ 'Knowledge of medical equipment and veterinary terminology is necessary.',
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+ 'Worked as a pet trainer for obedience classes',
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+ 'Skilled in component sorting for various projects',
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+ ]
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+ embeddings = model.encode(sentences)
151
+ print(embeddings.shape)
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+ # [3, 768]
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+
154
+ # Get the similarity scores for the embeddings
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+ similarities = model.similarity(embeddings, embeddings)
156
+ print(similarities.shape)
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+ # [3, 3]
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+ ```
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+
160
+ <!--
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+ ### Direct Usage (Transformers)
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+
163
+ <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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+
168
+ <!--
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+ ### Downstream Usage (Sentence Transformers)
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+
171
+ You can finetune this model on your own dataset.
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+
173
+ <details><summary>Click to expand</summary>
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+
175
+ </details>
176
+ -->
177
+
178
+ <!--
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+ ### Out-of-Scope Use
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+
181
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+ -->
183
+
184
+ ## Evaluation
185
+
186
+ ### Metrics
187
+
188
+ #### Semantic Similarity
189
+ * Dataset: `sts-test`
190
+ * 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 | 0.8711 |
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+ | **spearman_cosine** | **0.827** |
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+ | pearson_manhattan | 0.851 |
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+ | spearman_manhattan | 0.8225 |
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+ | pearson_euclidean | 0.8564 |
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+ | spearman_euclidean | 0.8222 |
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+ | pearson_dot | 0.8482 |
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+ | spearman_dot | 0.8223 |
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+ | pearson_max | 0.8711 |
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+ | spearman_max | 0.827 |
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+
205
+ <!--
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+ ## Bias, Risks and Limitations
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+
208
+ *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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+
211
+ <!--
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+ ### Recommendations
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+
214
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
217
+ ## 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: 8,137 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 | int |
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+ | details | <ul><li>min: 6 tokens</li><li>mean: 16.7 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.46 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>0: ~49.50%</li><li>1: ~50.50%</li></ul> |
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+ * Samples:
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+ | sentence1 | sentence2 | score |
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+ |:-------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------|
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+ | <code>Ability to use tools such as power drills as required for the job.</code> | <code>Proficient in operating power tools for installation tasks</code> | <code>1</code> |
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+ | <code>Experience with networking, specifically the TCP/IP stack, routing, ports, and services is essential.</code> | <code>Designed user interfaces for web applications</code> | <code>0</code> |
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+ | <code>Ability to establish and maintain positive relationships with coaches, student-athletes, and vendors regarding equipment selection.</code> | <code>Developed strong partnerships with vendors forEquipment procurement</code> | <code>1</code> |
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+ * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
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+ ```json
239
+ {
240
+ "loss_fct": "torch.nn.modules.loss.MSELoss"
241
+ }
242
+ ```
243
+
244
+ ### Evaluation Dataset
245
+
246
+ #### Unnamed Dataset
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+
248
+
249
+ * Size: 2,035 evaluation samples
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+ * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
251
+ * Approximate statistics based on the first 1000 samples:
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+ | | sentence1 | sentence2 | score |
253
+ |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
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+ | type | string | string | int |
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+ | details | <ul><li>min: 6 tokens</li><li>mean: 16.2 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.47 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>0: ~48.10%</li><li>1: ~51.90%</li></ul> |
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+ * Samples:
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+ | sentence1 | sentence2 | score |
258
+ |:----------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------|:---------------|
259
+ | <code>Experience with vulnerability management tools like Nessus and Nexpose.</code> | <code>managed network configurations</code> | <code>0</code> |
260
+ | <code>Willingness to obtain a Texas fire extinguishers license as necessary.</code> | <code>Currently pursuing a Texas fire extinguishers license</code> | <code>1</code> |
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+ | <code>Experience in defining and maintaining enterprise architecture that supports business scalability.</code> | <code>Led the development of enterprise architecture frameworks for a multinational corporation</code> | <code>1</code> |
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+ * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
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+ ```json
264
+ {
265
+ "loss_fct": "torch.nn.modules.loss.MSELoss"
266
+ }
267
+ ```
268
+
269
+ ### Training Hyperparameters
270
+ #### Non-Default Hyperparameters
271
+
272
+ - `eval_strategy`: steps
273
+ - `per_device_train_batch_size`: 128
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+ - `per_device_eval_batch_size`: 128
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+ - `num_train_epochs`: 1
276
+ - `warmup_ratio`: 0.1
277
+
278
+ #### All Hyperparameters
279
+ <details><summary>Click to expand</summary>
280
+
281
+ - `overwrite_output_dir`: False
282
+ - `do_predict`: False
283
+ - `eval_strategy`: steps
284
+ - `prediction_loss_only`: True
285
+ - `per_device_train_batch_size`: 128
286
+ - `per_device_eval_batch_size`: 128
287
+ - `per_gpu_train_batch_size`: None
288
+ - `per_gpu_eval_batch_size`: None
289
+ - `gradient_accumulation_steps`: 1
290
+ - `eval_accumulation_steps`: None
291
+ - `learning_rate`: 5e-05
292
+ - `weight_decay`: 0.0
293
+ - `adam_beta1`: 0.9
294
+ - `adam_beta2`: 0.999
295
+ - `adam_epsilon`: 1e-08
296
+ - `max_grad_norm`: 1.0
297
+ - `num_train_epochs`: 1
298
+ - `max_steps`: -1
299
+ - `lr_scheduler_type`: linear
300
+ - `lr_scheduler_kwargs`: {}
301
+ - `warmup_ratio`: 0.1
302
+ - `warmup_steps`: 0
303
+ - `log_level`: passive
304
+ - `log_level_replica`: warning
305
+ - `log_on_each_node`: True
306
+ - `logging_nan_inf_filter`: True
307
+ - `save_safetensors`: True
308
+ - `save_on_each_node`: False
309
+ - `save_only_model`: False
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+ - `restore_callback_states_from_checkpoint`: False
311
+ - `no_cuda`: False
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+ - `use_cpu`: False
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+ - `use_mps_device`: False
314
+ - `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
327
+ - `tpu_num_cores`: None
328
+ - `tpu_metrics_debug`: False
329
+ - `debug`: []
330
+ - `dataloader_drop_last`: False
331
+ - `dataloader_num_workers`: 0
332
+ - `dataloader_prefetch_factor`: None
333
+ - `past_index`: -1
334
+ - `disable_tqdm`: False
335
+ - `remove_unused_columns`: True
336
+ - `label_names`: None
337
+ - `load_best_model_at_end`: False
338
+ - `ignore_data_skip`: False
339
+ - `fsdp`: []
340
+ - `fsdp_min_num_params`: 0
341
+ - `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
346
+ - `optim`: adamw_torch
347
+ - `optim_args`: None
348
+ - `adafactor`: False
349
+ - `group_by_length`: False
350
+ - `length_column_name`: length
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+ - `ddp_find_unused_parameters`: None
352
+ - `ddp_bucket_cap_mb`: None
353
+ - `ddp_broadcast_buffers`: False
354
+ - `dataloader_pin_memory`: True
355
+ - `dataloader_persistent_workers`: False
356
+ - `skip_memory_metrics`: True
357
+ - `use_legacy_prediction_loop`: False
358
+ - `push_to_hub`: False
359
+ - `resume_from_checkpoint`: None
360
+ - `hub_model_id`: None
361
+ - `hub_strategy`: every_save
362
+ - `hub_private_repo`: False
363
+ - `hub_always_push`: False
364
+ - `gradient_checkpointing`: False
365
+ - `gradient_checkpointing_kwargs`: None
366
+ - `include_inputs_for_metrics`: False
367
+ - `eval_do_concat_batches`: True
368
+ - `fp16_backend`: auto
369
+ - `push_to_hub_model_id`: None
370
+ - `push_to_hub_organization`: None
371
+ - `mp_parameters`:
372
+ - `auto_find_batch_size`: False
373
+ - `full_determinism`: False
374
+ - `torchdynamo`: None
375
+ - `ray_scope`: last
376
+ - `ddp_timeout`: 1800
377
+ - `torch_compile`: False
378
+ - `torch_compile_backend`: None
379
+ - `torch_compile_mode`: None
380
+ - `dispatch_batches`: None
381
+ - `split_batches`: None
382
+ - `include_tokens_per_second`: False
383
+ - `include_num_input_tokens_seen`: False
384
+ - `neftune_noise_alpha`: None
385
+ - `optim_target_modules`: None
386
+ - `batch_eval_metrics`: False
387
+ - `batch_sampler`: batch_sampler
388
+ - `multi_dataset_batch_sampler`: proportional
389
+
390
+ </details>
391
+
392
+ ### Training Logs
393
+ | Epoch | Step | sts-test_spearman_cosine |
394
+ |:-----:|:----:|:------------------------:|
395
+ | 1.0 | 64 | 0.8270 |
396
+
397
+
398
+ ### Framework Versions
399
+ - Python: 3.10.11
400
+ - Sentence Transformers: 3.0.1
401
+ - Transformers: 4.41.2
402
+ - PyTorch: 2.3.1
403
+ - Accelerate: 0.31.0
404
+ - Datasets: 2.19.1
405
+ - Tokenizers: 0.19.1
406
+
407
+ ## Citation
408
+
409
+ ### BibTeX
410
+
411
+ #### Sentence Transformers
412
+ ```bibtex
413
+ @inproceedings{reimers-2019-sentence-bert,
414
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
415
+ author = "Reimers, Nils and Gurevych, Iryna",
416
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
417
+ month = "11",
418
+ year = "2019",
419
+ publisher = "Association for Computational Linguistics",
420
+ url = "https://arxiv.org/abs/1908.10084",
421
+ }
422
+ ```
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+
424
+ <!--
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+ ## Glossary
426
+
427
+ *Clearly define terms in order to be accessible across audiences.*
428
+ -->
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+
430
+ <!--
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+ ## Model Card Authors
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+
433
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
434
+ -->
435
+
436
+ <!--
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+ ## Model Card Contact
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+
439
+ *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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1
+ {
2
+ "_name_or_path": "distilroberta-base",
3
+ "architectures": [
4
+ "RobertaModel"
5
+ ],
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+ "attention_probs_dropout_prob": 0.1,
7
+ "bos_token_id": 0,
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+ "classifier_dropout": null,
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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": "roberta",
18
+ "num_attention_heads": 12,
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+ "num_hidden_layers": 6,
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+ "pad_token_id": 1,
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+ "position_embedding_type": "absolute",
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+ "torch_dtype": "float32",
23
+ "transformers_version": "4.41.2",
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+ "type_vocab_size": 1,
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+ "use_cache": true,
26
+ "vocab_size": 50265
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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": "2.3.1"
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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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+ }
merges.txt ADDED
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model.safetensors ADDED
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+ version https://git-lfs.github.com/spec/v1
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