
# SBERT-base-msmarco

## Usage

For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt 

## Evaluation Results

For eval results, refer to our paper: https://arxiv.org/abs/2202.08904

## Training
The model was trained with the parameters:

**DataLoader**:

`torch.utils.data.dataloader.DataLoader` of length 15600 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```

**Loss**:

`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
  ```
  {'scale': 20.0, 'similarity_fct': 'cos_sim'}
  ```

Parameters of the fit()-Method:
```
{
    "epochs": 10,
    "evaluation_steps": 0,
    "evaluator": "NoneType",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'transformers.optimization.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 1000,
    "weight_decay": 0.01
}
```


## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 300, 'do_lower_case': False}) with Transformer model: BertModel 
  (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})
)
```

## Citing & Authors

```bibtex
@article{muennighoff2022sgpt,
  title={SGPT: GPT Sentence Embeddings for Semantic Search},
  author={Muennighoff, Niklas},
  journal={arXiv preprint arXiv:2202.08904},
  year={2022}
}
```

{"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"}
sentence-transformers pytorch bert feature-extraction sentence-similarity transformers arxiv:2202.08904 endpoints_compatible region:us
TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-2202.08904 #endpoints_compatible #region-us 


# SBERT-base-msmarco

## Usage

For usage instructions, refer to our codebase: URL 

## Evaluation Results

For eval results, refer to our paper: URL

## Training
The model was trained with the parameters:

DataLoader:

'URL.dataloader.DataLoader' of length 15600 with parameters:


Loss:

'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:
  

Parameters of the fit()-Method:



## Full Model Architecture


## Citing & Authors



# SBERT-base-msmarco ## Usage

For usage instructions, refer to our codebase: URL ## Evaluation Results

For eval results, refer to our paper: URL ## Training
The model was trained with the parameters:

DataLoader:

'URL.dataloader.DataLoader' of length 15600 with parameters:


Loss:

'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:
  

Parameters of the fit()-Method: ## Full Model Architecture ## Citing & Authors
TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-2202.08904 #endpoints_compatible #region-us 
 # SBERT-base-msmarco ## Usage

For usage instructions, refer to our codebase: URL ## Evaluation Results

For eval results, refer to our paper: URL ## Training
The model was trained with the parameters:

DataLoader:

'URL.dataloader.DataLoader' of length 15600 with parameters:


Loss:

'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:
  

Parameters of the fit()-Method: ## Full Model Architecture ## Citing & Authors
passage: TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-2202.08904 #endpoints_compatible #region-us 
# SBERT-base-msmarco## Usage

For usage instructions, refer to our codebase: URL## Evaluation Results

For eval results, refer to our paper: URL## Training
The model was trained with the parameters:

DataLoader:

'URL.dataloader.DataLoader' of length 15600 with parameters:


Loss:

'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:
  

Parameters of the fit()-Method:## Full Model Architecture## Citing & Authors