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--- |
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language: [] |
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library_name: sentence-transformers |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- dataset_size:10K<n<100K |
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- loss:CosineSimilarityLoss |
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base_model: distilbert/distilbert-base-uncased |
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metrics: |
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- pearson_cosine |
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- spearman_cosine |
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- pearson_manhattan |
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- spearman_manhattan |
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- pearson_euclidean |
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- spearman_euclidean |
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- pearson_dot |
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- spearman_dot |
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- pearson_max |
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- spearman_max |
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widget: |
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- source_sentence: The long jump pit had to be raked after every few attempts. |
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sentences: |
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- The high jumper cleared the bar on his first attempt. |
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- Chemists use quantum mechanics to predict electron behavior and molecular bonding. |
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- Eczema frequently appears as inflamed, tender spots on several parts of the body. |
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- source_sentence: Street art transforms empty rural barns into lively murals. |
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sentences: |
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- Traditional folk music plays a significant role in preserving a community's history. |
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- '[SYNTAX] The saxophone offers the high-pitched, thrilling elements in a jazz |
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trio.' |
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- Atmospheric pressure decreases as you move higher above sea level. |
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- source_sentence: Proteins are synthesized through the process of translation. |
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sentences: |
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- Molecular genetics studies the structure and function of genes at a molecular |
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level. |
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- The mathematics lecture is a compelling method for introducing integral equations. |
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- 'The correlation between air pollution and increased mortality rates is well-documented. ' |
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- source_sentence: '[SYNTAX] A barometer is used to measure atmospheric pressure.' |
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sentences: |
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- '[SYNTAX] Colonialism is a primary subject in several political science research |
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papers.' |
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- '[SYNTAX] Ordinary urban walls are turned into vibrant masterpieces by street |
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art.' |
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- Email remains a significant device for academic and fictional correspondence. |
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- source_sentence: Salinity gradients in oceans affect local wildlife habitats. |
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sentences: |
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- The distribution of wildlife in different habitats has fascinated ecologists for |
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decades. |
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- '[SYNTAX] Bioenergy plants can convert agricultural waste into valuable electricity.' |
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- Proper management of irrigation schedules is crucial for crop health. |
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pipeline_tag: sentence-similarity |
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model-index: |
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- name: SentenceTransformer based on distilbert/distilbert-base-uncased |
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results: |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: custom dev |
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type: custom-dev |
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metrics: |
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- type: pearson_cosine |
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value: 0.9117000984572255 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.8442193394453843 |
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name: Spearman Cosine |
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- type: pearson_manhattan |
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value: 0.9156511082976959 |
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name: Pearson Manhattan |
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- type: spearman_manhattan |
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value: 0.8440889792296263 |
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name: Spearman Manhattan |
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- type: pearson_euclidean |
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value: 0.9159884478218315 |
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name: Pearson Euclidean |
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- type: spearman_euclidean |
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value: 0.8445673615230997 |
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name: Spearman Euclidean |
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- type: pearson_dot |
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value: 0.9046139794819923 |
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name: Pearson Dot |
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- type: spearman_dot |
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value: 0.8327655787489855 |
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name: Spearman Dot |
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- type: pearson_max |
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value: 0.9159884478218315 |
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name: Pearson Max |
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- type: spearman_max |
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value: 0.8445673615230997 |
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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: custom test |
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type: custom-test |
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metrics: |
|
- type: pearson_cosine |
|
value: 0.919801732989496 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.8500534773438543 |
|
name: Spearman Cosine |
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- type: pearson_manhattan |
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value: 0.9282084953416339 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.8493690342081703 |
|
name: Spearman Manhattan |
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- type: pearson_euclidean |
|
value: 0.9284184436823353 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.849759760833697 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.9141474471982576 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.8410969822964006 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.9284184436823353 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.8500534773438543 |
|
name: Spearman Max |
|
--- |
|
|
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# SentenceTransformer based on distilbert/distilbert-base-uncased |
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|
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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|
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## Model Details |
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|
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) <!-- at revision 12040accade4e8a0f71eabdb258fecc2e7e948be --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 tokens |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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|
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### Model Sources |
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|
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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|
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### Full Model Architecture |
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|
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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) |
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``` |
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|
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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|
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```bash |
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pip install -U sentence-transformers |
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``` |
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|
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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|
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# Download from the 🤗 Hub |
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model = SentenceTransformer("sentence_transformers_model_id") |
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# Run inference |
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sentences = [ |
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'Salinity gradients in oceans affect local wildlife habitats.', |
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'The distribution of wildlife in different habitats has fascinated ecologists for decades.', |
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'[SYNTAX] Bioenergy plants can convert agricultural waste into valuable electricity.', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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|
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### 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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|
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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|
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## Evaluation |
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|
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### Metrics |
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#### Semantic Similarity |
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* Dataset: `custom-dev` |
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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 | 0.9117 | |
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| **spearman_cosine** | **0.8442** | |
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| pearson_manhattan | 0.9157 | |
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| spearman_manhattan | 0.8441 | |
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| pearson_euclidean | 0.916 | |
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| spearman_euclidean | 0.8446 | |
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| pearson_dot | 0.9046 | |
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| spearman_dot | 0.8328 | |
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| pearson_max | 0.916 | |
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| spearman_max | 0.8446 | |
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|
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#### Semantic Similarity |
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* Dataset: `custom-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 | 0.9198 | |
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| **spearman_cosine** | **0.8501** | |
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| pearson_manhattan | 0.9282 | |
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| spearman_manhattan | 0.8494 | |
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| pearson_euclidean | 0.9284 | |
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| spearman_euclidean | 0.8498 | |
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| pearson_dot | 0.9141 | |
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| spearman_dot | 0.8411 | |
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| pearson_max | 0.9284 | |
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| spearman_max | 0.8501 | |
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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: 19,352 training samples |
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* Columns: <code>s1</code>, <code>s2</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | s1 | s2 | label | |
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|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------| |
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| type | string | string | int | |
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| details | <ul><li>min: 10 tokens</li><li>mean: 19.92 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 20.53 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>0: ~50.50%</li><li>1: ~49.50%</li></ul> | |
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* Samples: |
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| s1 | s2 | label | |
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|:-----------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------|:---------------| |
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| <code>According to labeling theory, individuals are considered deviant once society has tagged them with that label.</code> | <code>Labeling theory posits that corporations become powerful when labeled as such by stakeholders.</code> | <code>0</code> | |
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| <code>Employers must classify workers correctly as either employees or independent contractors to comply with tax and labor laws.</code> | <code>Employers must classify workers correctly as either employees or independent contractors to comply with tax and labor laws.</code> | <code>1</code> | |
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| <code>Higher education institutions play a critical role in advancing research and innovation.</code> | <code>Advancement in research and innovation is significantly driven by the contributions of higher education institutions.</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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|
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### Evaluation Dataset |
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#### Unnamed Dataset |
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* Size: 2,419 evaluation samples |
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* Columns: <code>s1</code>, <code>s2</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | s1 | s2 | label | |
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|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------| |
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| type | string | string | int | |
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| details | <ul><li>min: 11 tokens</li><li>mean: 19.91 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 20.46 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>0: ~49.70%</li><li>1: ~50.30%</li></ul> | |
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* Samples: |
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| s1 | s2 | label | |
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|:----------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------|:---------------| |
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| <code>Acoustic tomography is an innovative geophysical technique used to image the Earth's interior.</code> | <code>Acoustic tomography is an innovative geophysical technique used to image the Earth's interior.</code> | <code>1</code> | |
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| <code>Urban areas frequently exhibit a different age distribution pattern compared to rural areas.</code> | <code>Urban areas frequently exhibit a different age distribution pattern compared to rural areas.</code> | <code>1</code> | |
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| <code>Radiocarbon dating is a critical tool for assessing the duration of battery life in modern electronic devices.</code> | <code>Radiocarbon dating is a critical tool for assessing the duration of battery life in modern electronic devices.</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: |
|
```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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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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|
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `num_train_epochs`: 10 |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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|
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#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
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|
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 10 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: True |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
|
- `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 |
|
- `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 |
|
- `push_to_hub_organization`: None |
|
- `mp_parameters`: |
|
- `auto_find_batch_size`: False |
|
- `full_determinism`: False |
|
- `torchdynamo`: None |
|
- `ray_scope`: last |
|
- `ddp_timeout`: 1800 |
|
- `torch_compile`: False |
|
- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
|
- `batch_sampler`: batch_sampler |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
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|
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### Training Logs |
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| Epoch | Step | Training Loss | loss | custom-dev_spearman_cosine | custom-test_spearman_cosine | |
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|:------:|:----:|:-------------:|:------:|:--------------------------:|:---------------------------:| |
|
| 0.3300 | 100 | 0.2961 | 0.1185 | 0.8063 | - | |
|
| 0.6601 | 200 | 0.0772 | 0.0504 | 0.8461 | - | |
|
| 0.9901 | 300 | 0.0502 | 0.0454 | 0.8486 | - | |
|
| 1.3201 | 400 | 0.0376 | 0.0402 | 0.8481 | - | |
|
| 1.6502 | 500 | 0.0344 | 0.0400 | 0.8501 | - | |
|
| 1.9802 | 600 | 0.0329 | 0.0390 | 0.8518 | - | |
|
| 2.3102 | 700 | 0.0185 | 0.0387 | 0.8496 | - | |
|
| 2.6403 | 800 | 0.0164 | 0.0371 | 0.8492 | - | |
|
| 2.9703 | 900 | 0.0179 | 0.0393 | 0.8428 | - | |
|
| 3.3003 | 1000 | 0.0099 | 0.0389 | 0.8466 | - | |
|
| 3.6304 | 1100 | 0.0092 | 0.0395 | 0.8480 | - | |
|
| 3.9604 | 1200 | 0.0101 | 0.0368 | 0.8492 | - | |
|
| 4.2904 | 1300 | 0.0067 | 0.0385 | 0.8474 | - | |
|
| 4.6205 | 1400 | 0.0056 | 0.0393 | 0.8456 | - | |
|
| 4.9505 | 1500 | 0.0068 | 0.0401 | 0.8466 | - | |
|
| 5.2805 | 1600 | 0.0041 | 0.0410 | 0.8462 | - | |
|
| 5.6106 | 1700 | 0.0043 | 0.0399 | 0.8469 | - | |
|
| 5.9406 | 1800 | 0.0039 | 0.0406 | 0.8463 | - | |
|
| 6.2706 | 1900 | 0.003 | 0.0400 | 0.8456 | - | |
|
| 6.6007 | 2000 | 0.0026 | 0.0416 | 0.8438 | - | |
|
| 6.9307 | 2100 | 0.0027 | 0.0420 | 0.8437 | - | |
|
| 7.2607 | 2200 | 0.0028 | 0.0424 | 0.8449 | - | |
|
| 7.5908 | 2300 | 0.0021 | 0.0422 | 0.8458 | - | |
|
| 7.9208 | 2400 | 0.002 | 0.0414 | 0.8451 | - | |
|
| 8.2508 | 2500 | 0.0015 | 0.0421 | 0.8451 | - | |
|
| 8.5809 | 2600 | 0.0015 | 0.0427 | 0.8451 | - | |
|
| 8.9109 | 2700 | 0.0016 | 0.0429 | 0.8444 | - | |
|
| 9.2409 | 2800 | 0.0011 | 0.0432 | 0.8442 | - | |
|
| 9.5710 | 2900 | 0.0014 | 0.0432 | 0.8444 | - | |
|
| 9.9010 | 3000 | 0.0011 | 0.0432 | 0.8442 | - | |
|
| 10.0 | 3030 | - | - | - | 0.8501 | |
|
|
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|
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### Framework Versions |
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- Python: 3.11.9 |
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- Sentence Transformers: 3.0.0 |
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- Transformers: 4.41.2 |
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- PyTorch: 2.3.0+cu121 |
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- Accelerate: 0.30.1 |
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- Datasets: 2.19.1 |
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- Tokenizers: 0.19.1 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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