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--- |
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base_model: llmrails/ember-v1 |
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datasets: [] |
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language: [] |
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library_name: sentence-transformers |
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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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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:17500 |
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- loss:ContrastiveLoss |
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widget: |
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- source_sentence: 260 Mount Prospect Apt A4 |
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sentences: |
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- 254 Mount Apt 304 |
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- '110 Nightin - Gale #10' |
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- 3100 35 Apt 2 |
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- source_sentence: '20 Harding #2' |
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sentences: |
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- '1208 Barclay #2' |
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- '65 Chestnut # 72' |
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- '20 Harding # 2' |
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- source_sentence: 396 Manila Apt 2B |
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sentences: |
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- 108 Gaston 1 |
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- '175 2nd #710' |
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- '1 - 02 Virginia #102B' |
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- source_sentence: '210 Gordon Fl #1' |
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sentences: |
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- 450 Raritan Ste C |
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- '19 Edsall #1' |
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- '210 Gordon # 1' |
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- source_sentence: '148 1 / 2 Mill #B' |
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sentences: |
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- '7918 Pershing #' |
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- '91 5th #1' |
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- 148 1 / 2 Mill Apt B |
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model-index: |
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- name: SentenceTransformer based on llmrails/ember-v1 |
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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: test |
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type: test |
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metrics: |
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- type: pearson_cosine |
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value: 0.7022202945624949 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.5521115900667813 |
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name: Spearman Cosine |
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- type: pearson_manhattan |
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value: 0.5902198760799219 |
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name: Pearson Manhattan |
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- type: spearman_manhattan |
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value: 0.5601831188247873 |
|
name: Spearman Manhattan |
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- type: pearson_euclidean |
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value: 0.5899864734850421 |
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name: Pearson Euclidean |
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- type: spearman_euclidean |
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value: 0.5598213668477258 |
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name: Spearman Euclidean |
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- type: pearson_dot |
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value: 0.5811885721377421 |
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name: Pearson Dot |
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- type: spearman_dot |
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value: 0.45745466821334696 |
|
name: Spearman Dot |
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- type: pearson_max |
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value: 0.7022202945624949 |
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name: Pearson Max |
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- type: spearman_max |
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value: 0.5601831188247873 |
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name: Spearman Max |
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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: validation |
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type: validation |
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metrics: |
|
- type: pearson_cosine |
|
value: 0.9438614062035536 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.6566095423715015 |
|
name: Spearman Cosine |
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- type: pearson_manhattan |
|
value: 0.9661648909940331 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
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value: 0.6545897461863388 |
|
name: Spearman Manhattan |
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- type: pearson_euclidean |
|
value: 0.9662831349240031 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
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value: 0.6545799233453334 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.7832494697144132 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.6240051940767 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.9662831349240031 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.6566095423715015 |
|
name: Spearman Max |
|
--- |
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|
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# SentenceTransformer based on llmrails/ember-v1 |
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|
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [llmrails/ember-v1](https://huggingface.co/llmrails/ember-v1). It maps sentences & paragraphs to a 1024-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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|
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [llmrails/ember-v1](https://huggingface.co/llmrails/ember-v1) <!-- at revision 5e5ce5904901f6ce1c353a95020f17f09e5d021d --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 1024 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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|
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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': True}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("jarredparrett/fine-tuned-address-model-ember-v1") |
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# Run inference |
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sentences = [ |
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'148 1 / 2 Mill #B', |
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'148 1 / 2 Mill Apt B', |
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'7918 Pershing #', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 1024] |
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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: `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 | 0.7022 | |
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| spearman_cosine | 0.5521 | |
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| pearson_manhattan | 0.5902 | |
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| spearman_manhattan | 0.5602 | |
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| pearson_euclidean | 0.59 | |
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| spearman_euclidean | 0.5598 | |
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| pearson_dot | 0.5812 | |
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| spearman_dot | 0.4575 | |
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| pearson_max | 0.7022 | |
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| **spearman_max** | **0.5602** | |
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|
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#### Semantic Similarity |
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* Dataset: `validation` |
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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 | 0.9439 | |
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| spearman_cosine | 0.6566 | |
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| pearson_manhattan | 0.9662 | |
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| spearman_manhattan | 0.6546 | |
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| pearson_euclidean | 0.9663 | |
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| spearman_euclidean | 0.6546 | |
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| pearson_dot | 0.7832 | |
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| spearman_dot | 0.624 | |
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| pearson_max | 0.9663 | |
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| **spearman_max** | **0.6566** | |
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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: 17,500 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: 5 tokens</li><li>mean: 6.97 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 6.96 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>0: ~17.80%</li><li>1: ~82.20%</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>94 Liberty 1</code> | <code>94 Liberty Flr 1</code> | <code>1</code> | |
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| <code>166 Randolph Apt 1</code> | <code>166 Randolph Flr 1</code> | <code>1</code> | |
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| <code>400 Dutch Apt E12</code> | <code>400 Dutch Neck Apt E12</code> | <code>1</code> | |
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* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters: |
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```json |
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{ |
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"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", |
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"margin": 0.5, |
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"size_average": true |
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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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- `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`: 4 |
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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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- `torch_empty_cache_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`: 4 |
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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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- `eval_on_start`: False |
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- `eval_use_gather_object`: False |
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- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: round_robin |
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</details> |
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|
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### Training Logs |
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| Epoch | Step | Training Loss | test_spearman_max | validation_spearman_max | |
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|:------:|:----:|:-------------:|:-----------------:|:-----------------------:| |
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| 0 | 0 | - | 0.5602 | - | |
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| 0.0914 | 100 | - | - | 0.6565 | |
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| 0.1828 | 200 | - | - | 0.6567 | |
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| 0.2742 | 300 | - | - | 0.6558 | |
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| 0.3656 | 400 | - | - | 0.6560 | |
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| 0.4570 | 500 | 0.0039 | - | 0.6560 | |
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| 0.5484 | 600 | - | - | 0.6555 | |
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| 0.6399 | 700 | - | - | 0.6559 | |
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| 0.7313 | 800 | - | - | 0.6561 | |
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| 0.8227 | 900 | - | - | 0.6555 | |
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| 0.9141 | 1000 | 0.0019 | - | 0.6558 | |
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| 1.0 | 1094 | - | - | 0.6560 | |
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| 1.0055 | 1100 | - | - | 0.6561 | |
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| 1.0969 | 1200 | - | - | 0.6560 | |
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| 1.1883 | 1300 | - | - | 0.6559 | |
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| 1.2797 | 1400 | - | - | 0.6555 | |
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| 1.3711 | 1500 | 0.0014 | - | 0.6558 | |
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| 1.4625 | 1600 | - | - | 0.6560 | |
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| 1.5539 | 1700 | - | - | 0.6557 | |
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| 1.6453 | 1800 | - | - | 0.6561 | |
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| 1.7367 | 1900 | - | - | 0.6561 | |
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| 1.8282 | 2000 | 0.001 | - | 0.6562 | |
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| 1.9196 | 2100 | - | - | 0.6563 | |
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| 2.0 | 2188 | - | - | 0.6564 | |
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| 2.0110 | 2200 | - | - | 0.6565 | |
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| 2.1024 | 2300 | - | - | 0.6565 | |
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| 2.1938 | 2400 | - | - | 0.6560 | |
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| 2.2852 | 2500 | 0.0009 | - | 0.6557 | |
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| 2.3766 | 2600 | - | - | 0.6559 | |
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| 2.4680 | 2700 | - | - | 0.6560 | |
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| 2.5594 | 2800 | - | - | 0.6560 | |
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| 2.6508 | 2900 | - | - | 0.6564 | |
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| 2.7422 | 3000 | 0.0007 | - | 0.6565 | |
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| 2.8336 | 3100 | - | - | 0.6565 | |
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| 2.9250 | 3200 | - | - | 0.6564 | |
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| 3.0 | 3282 | - | - | 0.6565 | |
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| 3.0165 | 3300 | - | - | 0.6566 | |
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| 3.1079 | 3400 | - | - | 0.6568 | |
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| 3.1993 | 3500 | 0.0007 | - | 0.6565 | |
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| 3.2907 | 3600 | - | - | 0.6563 | |
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| 3.3821 | 3700 | - | - | 0.6564 | |
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| 3.4735 | 3800 | - | - | 0.6565 | |
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| 3.5649 | 3900 | - | - | 0.6565 | |
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| 3.6563 | 4000 | 0.0005 | - | 0.6566 | |
|
| 3.7477 | 4100 | - | - | 0.6566 | |
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| 3.8391 | 4200 | - | - | 0.6566 | |
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| 3.9305 | 4300 | - | - | 0.6566 | |
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| 4.0 | 4376 | - | - | 0.6566 | |
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|
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|
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### Framework Versions |
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- Python: 3.10.12 |
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- Sentence Transformers: 3.0.1 |
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- Transformers: 4.44.2 |
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- PyTorch: 2.4.0+cu121 |
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- Accelerate: 0.33.0 |
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- Datasets: 2.21.0 |
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- Tokenizers: 0.19.1 |
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## Citation |
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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", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
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year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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|
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#### ContrastiveLoss |
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```bibtex |
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@inproceedings{hadsell2006dimensionality, |
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author={Hadsell, R. and Chopra, S. and LeCun, Y.}, |
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booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)}, |
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title={Dimensionality Reduction by Learning an Invariant Mapping}, |
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year={2006}, |
|
volume={2}, |
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number={}, |
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pages={1735-1742}, |
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doi={10.1109/CVPR.2006.100} |
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} |
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``` |
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*Clearly define terms in order to be accessible across audiences.* |
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