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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:38688
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- loss:ContrastiveLoss
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base_model: sentence-transformers/all-MiniLM-L6-v2
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datasets: []
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widget:
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- source_sentence: There is a heavy cost for this service provided in conjunction
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with NOAA and SARSAT.
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sentences:
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- No significant changes have been made to the roadway except for its legal definition.
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- Some academics have questioned the ethics of these payments.
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- There is no charge for this service provided in conjunction with NOAA and SARSAT.
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- source_sentence: You're not thin.
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sentences:
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- This process is called low-dimensional embedded in machine learning.
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- You're thin.
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- Jean Prouvost was the founder of Marie Claire.
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- source_sentence: The lead man is charisma-free.
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sentences:
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- Fossil egg s are rare, but one oogenus, Polyclonoolithus, was discovered in the
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Hekou Group.
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- The roof is shingled, and topped by a small belfry.
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- The lead man doesn't have charisma.
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- source_sentence: Willis has criticized the rules adopted by the RNC, particularly
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Rules 12, 16, and 40.
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sentences:
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- Willis has fully accepted the rules adopted by the RNC, particularly Rules 12,
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16, and 40.
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- I can't stop reading.
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- This force acts on water independently of the wind stress.
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- source_sentence: The publication was named after Sir James Joynton Smith.
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sentences:
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- Detailed specific information on the ongoing validation activities is being made
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available in related publications.
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- On November 25, 2012, Tom O'Brien was reinstated.
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- The publication took its name from its founder and chief financer Sir James Joynton
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Smith.
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pipeline_tag: sentence-similarity
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---
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# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L6-v2) <!-- at revision 8b3219a92973c328a8e22fadcfa821b5dc75636a -->
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- **Maximum Sequence Length:** 256 tokens
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- **Output Dimensionality:** 384 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': 256, 'do_lower_case': False}) with Transformer model: BertModel
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(1): Pooling({'word_embedding_dimension': 384, '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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(2): Normalize()
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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("LeoChiuu/all-MiniLM-L6-v2-negations")
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# Run inference
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sentences = [
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'The publication was named after Sir James Joynton Smith.',
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'The publication took its name from its founder and chief financer Sir James Joynton Smith.',
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"On November 25, 2012, Tom O'Brien was reinstated.",
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]
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embeddings = model.encode(sentences)
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print(embeddings.shape)
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# [3, 384]
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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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<!--
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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: 38,688 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: 15.94 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.96 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>0: ~48.50%</li><li>1: ~51.50%</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>No, that is impossible.</code> | <code>No, that is not possible.</code> | <code>0</code> |
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| <code>The building did indeed serve as a hof, according to the bone finds.</code> | <code>The bone finds thus indicate the building did indeed serve as a hof.</code> | <code>0</code> |
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| <code>The building became a pet shop.</code> | <code>The building became a hospital.</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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- `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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- `multi_dataset_batch_sampler`: round_robin
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#### All Hyperparameters
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<details><summary>Click to expand</summary>
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- `overwrite_output_dir`: False
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- `do_predict`: False
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- `prediction_loss_only`: True
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- `per_device_train_batch_size`: 16
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- `per_device_eval_batch_size`: 16
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- `per_gpu_train_batch_size`: None
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- `per_gpu_eval_batch_size`: None
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- `gradient_accumulation_steps`: 1
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- `eval_accumulation_steps`: None
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- `learning_rate`: 5e-05
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- `weight_decay`: 0.0
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- `adam_beta1`: 0.9
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- `adam_beta2`: 0.999
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- `adam_epsilon`: 1e-08
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- `max_grad_norm`: 1
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- `num_train_epochs`: 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`: 0.0
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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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- `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, '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_sampler`: batch_sampler
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- `multi_dataset_batch_sampler`: round_robin
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</details>
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### Training Logs
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| Epoch | Step | Training Loss |
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|:------:|:-----:|:-------------:|
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| 0.2068 | 500 | 0.0353 |
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| 0.4136 | 1000 | 0.0307 |
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| 0.6203 | 1500 | 0.0234 |
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| 0.8271 | 2000 | 0.0187 |
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| 1.0339 | 2500 | 0.0152 |
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| 1.2407 | 3000 | 0.0134 |
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| 1.4475 | 3500 | 0.0123 |
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| 1.6543 | 4000 | 0.0111 |
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| 1.8610 | 4500 | 0.0107 |
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| 2.0678 | 5000 | 0.0097 |
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| 2.2746 | 5500 | 0.0096 |
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| 2.4814 | 6000 | 0.0091 |
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| 2.6882 | 6500 | 0.0087 |
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| 2.8950 | 7000 | 0.0086 |
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| 3.1017 | 7500 | 0.0075 |
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| 3.3085 | 8000 | 0.008 |
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| 3.5153 | 8500 | 0.0074 |
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| 3.7221 | 9000 | 0.007 |
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| 3.9289 | 9500 | 0.007 |
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| 4.1356 | 10000 | 0.0063 |
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| 4.3424 | 10500 | 0.0068 |
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| 4.5492 | 11000 | 0.0061 |
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| 4.7560 | 11500 | 0.0059 |
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| 4.9628 | 12000 | 0.0056 |
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| 5.1696 | 12500 | 0.0052 |
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| 5.3763 | 13000 | 0.0055 |
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| 5.5831 | 13500 | 0.0051 |
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| 5.7899 | 14000 | 0.005 |
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| 5.9967 | 14500 | 0.0047 |
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| 6.2035 | 15000 | 0.0046 |
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| 6.4103 | 15500 | 0.0047 |
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| 6.6170 | 16000 | 0.0044 |
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| 6.8238 | 16500 | 0.0044 |
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| 7.0306 | 17000 | 0.0041 |
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| 7.2374 | 17500 | 0.004 |
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| 7.4442 | 18000 | 0.0044 |
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| 7.6510 | 18500 | 0.0039 |
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| 7.8577 | 19000 | 0.0038 |
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| 8.0645 | 19500 | 0.0038 |
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| 8.2713 | 20000 | 0.0037 |
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| 8.4781 | 20500 | 0.0039 |
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| 8.6849 | 21000 | 0.0037 |
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| 8.8916 | 21500 | 0.0036 |
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| 9.0984 | 22000 | 0.0034 |
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| 9.3052 | 22500 | 0.0036 |
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| 9.5120 | 23000 | 0.0035 |
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| 9.7188 | 23500 | 0.0034 |
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| 9.9256 | 24000 | 0.0035 |
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### Framework Versions
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- Python: 3.11.9
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- Sentence Transformers: 3.0.1
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- Transformers: 4.40.2
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- PyTorch: 2.3.0+cpu
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- Accelerate: 0.32.1
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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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#### 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},
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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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