Add new SentenceTransformer model.
Browse files- 1_Pooling/config.json +10 -0
- README.md +440 -0
- config.json +27 -0
- config_sentence_transformers.json +10 -0
- merges.txt +0 -0
- model.safetensors +3 -0
- modules.json +14 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +15 -0
- tokenizer.json +0 -0
- tokenizer_config.json +57 -0
- vocab.json +0 -0
1_Pooling/config.json
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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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}
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README.md
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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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# SentenceTransformer based on distilbert/distilroberta-base
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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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## Model Details
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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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### Model Sources
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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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### Full Model Architecture
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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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## Usage
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### Direct Usage (Sentence Transformers)
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First install the Sentence Transformers library:
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```bash
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pip install -U sentence-transformers
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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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# 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)
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print(embeddings.shape)
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# [3, 768]
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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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### Direct Usage (Transformers)
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<details><summary>Click to see the direct usage in Transformers</summary>
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</details>
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-->
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<!--
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### Downstream Usage (Sentence Transformers)
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You can finetune this model on your own dataset.
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<details><summary>Click to expand</summary>
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</details>
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-->
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<!--
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### Out-of-Scope Use
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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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## Evaluation
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### Metrics
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#### Semantic Similarity
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* Dataset: `sts-test`
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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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| 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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<!--
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## Bias, Risks and Limitations
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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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### Recommendations
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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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## Training Details
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### Training Dataset
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#### Unnamed Dataset
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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
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{
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"loss_fct": "torch.nn.modules.loss.MSELoss"
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}
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```
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### Evaluation Dataset
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#### Unnamed Dataset
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|
248 |
+
|
249 |
+
* Size: 2,035 evaluation samples
|
250 |
+
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
|
251 |
+
* Approximate statistics based on the first 1000 samples:
|
252 |
+
| | sentence1 | sentence2 | score |
|
253 |
+
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
|
254 |
+
| type | string | string | int |
|
255 |
+
| 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> |
|
256 |
+
* Samples:
|
257 |
+
| 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> |
|
261 |
+
| <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> |
|
262 |
+
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
|
263 |
+
```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
|
274 |
+
- `per_device_eval_batch_size`: 128
|
275 |
+
- `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
|
310 |
+
- `restore_callback_states_from_checkpoint`: False
|
311 |
+
- `no_cuda`: False
|
312 |
+
- `use_cpu`: False
|
313 |
+
- `use_mps_device`: False
|
314 |
+
- `seed`: 42
|
315 |
+
- `data_seed`: None
|
316 |
+
- `jit_mode_eval`: False
|
317 |
+
- `use_ipex`: False
|
318 |
+
- `bf16`: False
|
319 |
+
- `fp16`: False
|
320 |
+
- `fp16_opt_level`: O1
|
321 |
+
- `half_precision_backend`: auto
|
322 |
+
- `bf16_full_eval`: False
|
323 |
+
- `fp16_full_eval`: False
|
324 |
+
- `tf32`: None
|
325 |
+
- `local_rank`: 0
|
326 |
+
- `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}
|
342 |
+
- `fsdp_transformer_layer_cls_to_wrap`: None
|
343 |
+
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
|
344 |
+
- `deepspeed`: None
|
345 |
+
- `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
|
351 |
+
- `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 |
+
```
|
423 |
+
|
424 |
+
<!--
|
425 |
+
## Glossary
|
426 |
+
|
427 |
+
*Clearly define terms in order to be accessible across audiences.*
|
428 |
+
-->
|
429 |
+
|
430 |
+
<!--
|
431 |
+
## Model Card Authors
|
432 |
+
|
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 |
+
<!--
|
437 |
+
## Model Card Contact
|
438 |
+
|
439 |
+
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
440 |
+
-->
|
config.json
ADDED
@@ -0,0 +1,27 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"_name_or_path": "distilroberta-base",
|
3 |
+
"architectures": [
|
4 |
+
"RobertaModel"
|
5 |
+
],
|
6 |
+
"attention_probs_dropout_prob": 0.1,
|
7 |
+
"bos_token_id": 0,
|
8 |
+
"classifier_dropout": null,
|
9 |
+
"eos_token_id": 2,
|
10 |
+
"hidden_act": "gelu",
|
11 |
+
"hidden_dropout_prob": 0.1,
|
12 |
+
"hidden_size": 768,
|
13 |
+
"initializer_range": 0.02,
|
14 |
+
"intermediate_size": 3072,
|
15 |
+
"layer_norm_eps": 1e-05,
|
16 |
+
"max_position_embeddings": 514,
|
17 |
+
"model_type": "roberta",
|
18 |
+
"num_attention_heads": 12,
|
19 |
+
"num_hidden_layers": 6,
|
20 |
+
"pad_token_id": 1,
|
21 |
+
"position_embedding_type": "absolute",
|
22 |
+
"torch_dtype": "float32",
|
23 |
+
"transformers_version": "4.41.2",
|
24 |
+
"type_vocab_size": 1,
|
25 |
+
"use_cache": true,
|
26 |
+
"vocab_size": 50265
|
27 |
+
}
|
config_sentence_transformers.json
ADDED
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"__version__": {
|
3 |
+
"sentence_transformers": "3.0.1",
|
4 |
+
"transformers": "4.41.2",
|
5 |
+
"pytorch": "2.3.1"
|
6 |
+
},
|
7 |
+
"prompts": {},
|
8 |
+
"default_prompt_name": null,
|
9 |
+
"similarity_fn_name": null
|
10 |
+
}
|
merges.txt
ADDED
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|
|
model.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:84f5ba96b58901e85a0a423f2d3d145cbfe7bc403b819ae4c283e91e89343a73
|
3 |
+
size 328485128
|
modules.json
ADDED
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
[
|
2 |
+
{
|
3 |
+
"idx": 0,
|
4 |
+
"name": "0",
|
5 |
+
"path": "",
|
6 |
+
"type": "sentence_transformers.models.Transformer"
|
7 |
+
},
|
8 |
+
{
|
9 |
+
"idx": 1,
|
10 |
+
"name": "1",
|
11 |
+
"path": "1_Pooling",
|
12 |
+
"type": "sentence_transformers.models.Pooling"
|
13 |
+
}
|
14 |
+
]
|
sentence_bert_config.json
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"max_seq_length": 512,
|
3 |
+
"do_lower_case": false
|
4 |
+
}
|
special_tokens_map.json
ADDED
@@ -0,0 +1,15 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"bos_token": "<s>",
|
3 |
+
"cls_token": "<s>",
|
4 |
+
"eos_token": "</s>",
|
5 |
+
"mask_token": {
|
6 |
+
"content": "<mask>",
|
7 |
+
"lstrip": true,
|
8 |
+
"normalized": false,
|
9 |
+
"rstrip": false,
|
10 |
+
"single_word": false
|
11 |
+
},
|
12 |
+
"pad_token": "<pad>",
|
13 |
+
"sep_token": "</s>",
|
14 |
+
"unk_token": "<unk>"
|
15 |
+
}
|
tokenizer.json
ADDED
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|
|
tokenizer_config.json
ADDED
@@ -0,0 +1,57 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"add_prefix_space": false,
|
3 |
+
"added_tokens_decoder": {
|
4 |
+
"0": {
|
5 |
+
"content": "<s>",
|
6 |
+
"lstrip": false,
|
7 |
+
"normalized": true,
|
8 |
+
"rstrip": false,
|
9 |
+
"single_word": false,
|
10 |
+
"special": true
|
11 |
+
},
|
12 |
+
"1": {
|
13 |
+
"content": "<pad>",
|
14 |
+
"lstrip": false,
|
15 |
+
"normalized": true,
|
16 |
+
"rstrip": false,
|
17 |
+
"single_word": false,
|
18 |
+
"special": true
|
19 |
+
},
|
20 |
+
"2": {
|
21 |
+
"content": "</s>",
|
22 |
+
"lstrip": false,
|
23 |
+
"normalized": true,
|
24 |
+
"rstrip": false,
|
25 |
+
"single_word": false,
|
26 |
+
"special": true
|
27 |
+
},
|
28 |
+
"3": {
|
29 |
+
"content": "<unk>",
|
30 |
+
"lstrip": false,
|
31 |
+
"normalized": true,
|
32 |
+
"rstrip": false,
|
33 |
+
"single_word": false,
|
34 |
+
"special": true
|
35 |
+
},
|
36 |
+
"50264": {
|
37 |
+
"content": "<mask>",
|
38 |
+
"lstrip": true,
|
39 |
+
"normalized": false,
|
40 |
+
"rstrip": false,
|
41 |
+
"single_word": false,
|
42 |
+
"special": true
|
43 |
+
}
|
44 |
+
},
|
45 |
+
"bos_token": "<s>",
|
46 |
+
"clean_up_tokenization_spaces": true,
|
47 |
+
"cls_token": "<s>",
|
48 |
+
"eos_token": "</s>",
|
49 |
+
"errors": "replace",
|
50 |
+
"mask_token": "<mask>",
|
51 |
+
"model_max_length": 512,
|
52 |
+
"pad_token": "<pad>",
|
53 |
+
"sep_token": "</s>",
|
54 |
+
"tokenizer_class": "RobertaTokenizer",
|
55 |
+
"trim_offsets": true,
|
56 |
+
"unk_token": "<unk>"
|
57 |
+
}
|
vocab.json
ADDED
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|
|