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---
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base_model: google-bert/bert-base-uncased
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datasets:
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- stanfordnlp/snli
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language:
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- en
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library_name: sentence-transformers
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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:494430
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- loss:SoftmaxLoss
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widget:
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- source_sentence: A person out front of a business with a woman statue holding a
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bottle.
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sentences:
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- A couple holds hands.
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- The young boy is upside down.
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- the man is baking some bread
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- source_sentence: A person is dressed up in a weird costume with a red tongue sticking
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out.
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sentences:
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- thhe man plays a tuba
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- Four siblings are climbing on a fake black bear.
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- the tongue is blue
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- source_sentence: A man on a train is talking on a cellphone.
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sentences:
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- A man is playing a flute on a bus.
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- The woman is sexy.
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- two cyclists racing
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- source_sentence: An elderly woman giving her daughter a hug.
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sentences:
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- There are two women hugging.
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- A man holds a flag on the street.
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- people are sitting on a red roofed bus going to a museum
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- source_sentence: A pilot dressed in a dark-colored sweater is sitting in the cock-pit
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of a plane with his hands crossed.
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sentences:
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- A pilot is sitting in his plain with his hands crossed
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- The boys are playing outside on a log.
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- Two men discuss their love lives.
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---
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# SentenceTransformer based on google-bert/bert-base-uncased
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
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## Model Details
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### Model Description
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- **Model Type:** Sentence Transformer
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- **Base model:** [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) <!-- at revision 86b5e0934494bd15c9632b12f734a8a67f723594 -->
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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:**
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- [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli)
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- **Language:** en
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<!-- - **License:** Unknown -->
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### Model Sources
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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### Full Model Architecture
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```
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SentenceTransformer(
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
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)
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```
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## Usage
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### Direct Usage (Sentence Transformers)
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First install the Sentence Transformers library:
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```bash
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pip install -U sentence-transformers
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```
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Then you can load this model and run inference.
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```python
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from sentence_transformers import SentenceTransformer
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# Download from the 🤗 Hub
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model = SentenceTransformer("hcy5561/distilroberta-base-sentence-transformer-snli")
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# Run inference
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sentences = [
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'A pilot dressed in a dark-colored sweater is sitting in the cock-pit of a plane with his hands crossed.',
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'A pilot is sitting in his plain with his hands crossed',
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'The boys are playing outside on a log.',
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]
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embeddings = model.encode(sentences)
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print(embeddings.shape)
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# [3, 768]
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# Get the similarity scores for the embeddings
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similarities = model.similarity(embeddings, embeddings)
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print(similarities.shape)
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# [3, 3]
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```
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<!--
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### Direct Usage (Transformers)
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<details><summary>Click to see the direct usage in Transformers</summary>
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</details>
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-->
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<!--
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### Downstream Usage (Sentence Transformers)
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You can finetune this model on your own dataset.
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<details><summary>Click to expand</summary>
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</details>
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-->
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<!--
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### Out-of-Scope Use
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*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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-->
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<!--
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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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#### stanfordnlp/snli
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* Dataset: [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co/datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b)
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* Size: 494,430 training samples
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* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
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* Approximate statistics based on the first 1000 samples:
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| | premise | hypothesis | label |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
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| type | string | string | int |
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| details | <ul><li>min: 6 tokens</li><li>mean: 16.24 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.55 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>0: ~31.10%</li><li>1: ~33.40%</li><li>2: ~35.50%</li></ul> |
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* Samples:
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| premise | hypothesis | label |
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|:------------------------------------------------------------------------------|:---------------------------------------|:---------------|
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| <code>Two men, one in yellow, are on a wooden boat.</code> | <code>Two men swimming in water</code> | <code>2</code> |
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| <code>Two people sleep on a couch.</code> | <code>Two people are asleep.</code> | <code>0</code> |
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| <code>a little boy is learning to swim with the help of a float board.</code> | <code>The boy is crawling.</code> | <code>2</code> |
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* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
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### Evaluation Dataset
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#### stanfordnlp/snli
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* Dataset: [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co/datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b)
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* Size: 27,468 evaluation samples
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* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
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* Approximate statistics based on the first 1000 samples:
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| | premise | hypothesis | label |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
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| type | string | string | int |
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| details | <ul><li>min: 6 tokens</li><li>mean: 16.66 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.48 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>0: ~36.10%</li><li>1: ~31.80%</li><li>2: ~32.10%</li></ul> |
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* Samples:
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| premise | hypothesis | label |
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|:---------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------|:---------------|
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| <code>A taxi cab driver looks stressed out in his car.</code> | <code>a taxi driver is stressed</code> | <code>0</code> |
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| <code>Two men do trick in a park.</code> | <code>The men only sat on the bench in the park, doing nothing.</code> | <code>2</code> |
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| <code>Two woman walking, the blond is looking at the camera wearing sunglasses making an oh face.</code> | <code>One lady makes a shocked face at the camera as the photographer tells the women they are lost.</code> | <code>1</code> |
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* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
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### Training Hyperparameters
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#### Non-Default Hyperparameters
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- `per_device_train_batch_size`: 64
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- `per_device_eval_batch_size`: 64
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- `num_train_epochs`: 4
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- `warmup_ratio`: 0.1
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- `batch_sampler`: no_duplicates
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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`: 64
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- `per_device_eval_batch_size`: 64
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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`: 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`: 0.1
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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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- `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}
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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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- `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`: no_duplicates
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- `multi_dataset_batch_sampler`: proportional
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</details>
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### Training Logs
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| Epoch | Step | Training Loss | loss |
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|:------:|:-----:|:-------------:|:------:|
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| 0.1294 | 1000 | 0.9208 | 0.7448 |
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| 0.2589 | 2000 | 0.7095 | 0.6462 |
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| 0.3883 | 3000 | 0.6415 | 0.6199 |
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| 0.5177 | 4000 | 0.6125 | 0.5940 |
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| 0.6472 | 5000 | 0.5935 | 0.5672 |
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| 0.7766 | 6000 | 0.5748 | 0.5550 |
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| 0.9060 | 7000 | 0.5654 | 0.5506 |
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| 1.0355 | 8000 | 0.5524 | 0.5376 |
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| 1.1649 | 9000 | 0.5386 | 0.5319 |
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| 1.2943 | 10000 | 0.5192 | 0.5361 |
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| 1.4238 | 11000 | 0.4863 | 0.5304 |
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| 1.5532 | 12000 | 0.4687 | 0.5278 |
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| 1.6826 | 13000 | 0.4586 | 0.5305 |
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| 1.8121 | 14000 | 0.4474 | 0.5222 |
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| 1.9415 | 15000 | 0.4447 | 0.5237 |
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| 2.0709 | 16000 | 0.434 | 0.5172 |
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| 2.2004 | 17000 | 0.4243 | 0.5235 |
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| 2.3298 | 18000 | 0.398 | 0.5224 |
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| 2.4592 | 19000 | 0.3747 | 0.5344 |
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| 2.5887 | 20000 | 0.3669 | 0.5301 |
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| 2.7181 | 21000 | 0.3583 | 0.5406 |
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| 2.8475 | 22000 | 0.3496 | 0.5354 |
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| 2.9770 | 23000 | 0.3527 | 0.5324 |
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| 3.1064 | 24000 | 0.3419 | 0.5299 |
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| 3.2358 | 25000 | 0.3358 | 0.5456 |
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| 3.3653 | 26000 | 0.3096 | 0.5562 |
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| 3.4947 | 27000 | 0.2964 | 0.5644 |
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| 3.6241 | 28000 | 0.2998 | 0.5565 |
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| 3.7536 | 29000 | 0.2906 | 0.5590 |
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| 3.8830 | 30000 | 0.2923 | 0.5564 |
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### Framework Versions
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- Python: 3.10.6
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- Sentence Transformers: 3.0.1
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- Transformers: 4.39.3
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- PyTorch: 2.2.2+cu118
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- Accelerate: 0.28.0
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- Datasets: 2.20.0
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- Tokenizers: 0.15.2
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## Citation
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### BibTeX
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#### Sentence Transformers and SoftmaxLoss
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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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