
# 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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"mrr_at_1", "value": 32.653}, {"type": "mrr_at_10", "value": 48.083999999999996}, {"type": "mrr_at_100", "value": 48.631}, {"type": "mrr_at_1000", "value": 48.649}, {"type": "mrr_at_3", "value": 42.857}, {"type": "mrr_at_5", "value": 46.224}, {"type": "ndcg_at_1", "value": 29.592000000000002}, {"type": "ndcg_at_10", "value": 25.430999999999997}, {"type": "ndcg_at_100", "value": 36.344}, {"type": "ndcg_at_1000", "value": 47.676}, {"type": "ndcg_at_3", "value": 26.144000000000002}, {"type": "ndcg_at_5", "value": 26.304}, {"type": "precision_at_1", "value": 32.653}, {"type": "precision_at_10", "value": 24.082}, {"type": "precision_at_100", "value": 7.714}, {"type": "precision_at_1000", "value": 1.5310000000000001}, {"type": "precision_at_3", "value": 26.531}, {"type": "precision_at_5", "value": 26.939}, {"type": "recall_at_1", "value": 2.116}, {"type": "recall_at_10", "value": 16.794}, {"type": "recall_at_100", "value": 47.452}, {"type": "recall_at_1000", "value": 82.312}, {"type": "recall_at_3", "value": 5.306}, {"type": "recall_at_5", "value": 9.306000000000001}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB ToxicConversationsClassification", "type": "mteb/toxic_conversations_50k", "config": "default", "split": "test", "revision": "edfaf9da55d3dd50d43143d90c1ac476895ae6de"}, "metrics": [{"type": "accuracy", "value": 67.709}, {"type": "ap", "value": 13.541535578501716}, {"type": "f1", "value": 52.569619919446794}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB TweetSentimentExtractionClassification", "type": "mteb/tweet_sentiment_extraction", "config": "default", "split": "test", "revision": "62146448f05be9e52a36b8ee9936447ea787eede"}, "metrics": [{"type": "accuracy", "value": 56.850594227504246}, {"type": "f1", "value": 57.233377364910574}]}, {"task": {"type": "Clustering"}, "dataset": {"name": "MTEB TwentyNewsgroupsClustering", "type": "mteb/twentynewsgroups-clustering", "config": "default", "split": "test", "revision": "091a54f9a36281ce7d6590ec8c75dd485e7e01d4"}, "metrics": [{"type": "v_measure", "value": 39.463722986090474}]}, {"task": {"type": "PairClassification"}, "dataset": {"name": "MTEB TwitterSemEval2015", "type": "mteb/twittersemeval2015-pairclassification", "config": "default", "split": "test", "revision": "70970daeab8776df92f5ea462b6173c0b46fd2d1"}, "metrics": [{"type": "cos_sim_accuracy", "value": 84.09131549144662}, {"type": "cos_sim_ap", "value": 66.86677647503386}, {"type": "cos_sim_f1", "value": 62.94631710362049}, {"type": "cos_sim_precision", "value": 59.73933649289099}, {"type": "cos_sim_recall", "value": 66.51715039577837}, {"type": "dot_accuracy", "value": 80.27656911247541}, {"type": "dot_ap", "value": 54.291720398612085}, {"type": "dot_f1", "value": 54.77150537634409}, {"type": "dot_precision", "value": 47.58660957571039}, {"type": "dot_recall", "value": 64.5118733509235}, {"type": "euclidean_accuracy", "value": 82.76211480002385}, {"type": "euclidean_ap", "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