
# SGPT-5.8B-weightedmean-msmarco-specb-bitfit

## 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 249592 with parameters:
```
{'batch_size': 2, '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": 5e-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: GPTJModel 
  (1): Pooling({'word_embedding_dimension': 4096, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, '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}
}
```

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"value": 66.667}, {"type": "precision_at_5", "value": 62.8}, {"type": "recall_at_1", "value": 0.19499999999999998}, {"type": "recall_at_10", "value": 1.3639999999999999}, {"type": "recall_at_100", "value": 9.317}, {"type": "recall_at_1000", "value": 33.629999999999995}, {"type": "recall_at_3", "value": 0.49300000000000005}, {"type": "recall_at_5", "value": 0.756}]}, {"task": {"type": "Retrieval"}, "dataset": {"name": "MTEB Touche2020", "type": "webis-touche2020", "config": "default", "split": "test", "revision": "527b7d77e16e343303e68cb6af11d6e18b9f7b3b"}, "metrics": [{"type": "map_at_1", "value": 1.335}, {"type": "map_at_10", "value": 6.293}, {"type": "map_at_100", "value": 10.928}, {"type": "map_at_1000", "value": 12.359}, {"type": "map_at_3", "value": 3.472}, {"type": "map_at_5", "value": 4.935}, {"type": "ndcg_at_1", "value": 19.387999999999998}, {"type": "ndcg_at_10", "value": 16.178}, {"type": "ndcg_at_100", "value": 28.149}, {"type": "ndcg_at_1000", "value": 39.845000000000006}, {"type": "ndcg_at_3", "value": 19.171}, {"type": "ndcg_at_5", "value": 17.864}, {"type": "precision_at_1", "value": 20.408}, {"type": "precision_at_10", "value": 14.49}, {"type": "precision_at_100", "value": 6.306000000000001}, {"type": "precision_at_1000", "value": 1.3860000000000001}, {"type": "precision_at_3", "value": 21.088}, {"type": "precision_at_5", "value": 18.367}, {"type": "recall_at_1", "value": 1.335}, {"type": "recall_at_10", "value": 10.825999999999999}, {"type": "recall_at_100", "value": 39.251000000000005}, {"type": "recall_at_1000", "value": 74.952}, {"type": "recall_at_3", "value": 4.9110000000000005}, {"type": "recall_at_5", "value": 7.312}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB ToxicConversationsClassification", "type": "mteb/toxic_conversations_50k", "config": "default", "split": "test", "revision": "edfaf9da55d3dd50d43143d90c1ac476895ae6de"}, "metrics": [{"type": "accuracy", "value": 69.93339999999999}, {"type": "ap", "value": 13.87476602492533}, {"type": "f1", "value": 53.867357615848555}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB TweetSentimentExtractionClassification", "type": "mteb/tweet_sentiment_extraction", "config": "default", "split": "test", "revision": "62146448f05be9e52a36b8ee9936447ea787eede"}, "metrics": [{"type": "accuracy", "value": 62.43916242218449}, {"type": "f1", "value": 62.870386304954685}]}, {"task": {"type": "Clustering"}, "dataset": {"name": "MTEB TwentyNewsgroupsClustering", "type": "mteb/twentynewsgroups-clustering", "config": "default", "split": "test", "revision": "091a54f9a36281ce7d6590ec8c75dd485e7e01d4"}, "metrics": [{"type": "v_measure", "value": 37.202082549859796}]}, {"task": {"type": "PairClassification"}, "dataset": {"name": "MTEB TwitterSemEval2015", "type": "mteb/twittersemeval2015-pairclassification", "config": "default", "split": "test", "revision": "70970daeab8776df92f5ea462b6173c0b46fd2d1"}, "metrics": [{"type": "cos_sim_accuracy", "value": 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sentence-transformers pytorch gptj feature-extraction sentence-similarity mteb arxiv:2202.08904 model-index endpoints_compatible has_space region:us
TAGS
#sentence-transformers #pytorch #gptj #feature-extraction #sentence-similarity #mteb #arxiv-2202.08904 #model-index #endpoints_compatible #has_space #region-us 


# SGPT-5.8B-weightedmean-msmarco-specb-bitfit

## 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 249592 with parameters:


Loss:

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

Parameters of the fit()-Method:



## Full Model Architecture


## Citing & Authors



# SGPT-5.8B-weightedmean-msmarco-specb-bitfit ## 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 249592 with parameters:


Loss:

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

Parameters of the fit()-Method: ## Full Model Architecture ## Citing & Authors
TAGS
#sentence-transformers #pytorch #gptj #feature-extraction #sentence-similarity #mteb #arxiv-2202.08904 #model-index #endpoints_compatible #has_space #region-us 
 # SGPT-5.8B-weightedmean-msmarco-specb-bitfit ## 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 249592 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 #gptj #feature-extraction #sentence-similarity #mteb #arxiv-2202.08904 #model-index #endpoints_compatible #has_space #region-us 
# SGPT-5.8B-weightedmean-msmarco-specb-bitfit## 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 249592 with parameters:


Loss:

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

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