metadata
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
sgpt-nli-bloom-1b3
Usage
For usage instructions, refer to: https://github.com/Muennighoff/sgpt#symmetric-semantic-search
Evaluation Results
{'askubuntu': 57.44, 'cqadupstack': 14.18, 'twitterpara': 73.99, 'scidocs': 74.74, 'avg': 55.087500000000006}
Training
The model was trained with the parameters:
DataLoader:
sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader of length 4403 with parameters:
{'batch_size': 128}
Loss:
sentence_transformers.losses.MultipleNegativesRankingLoss.MNRLGradCache
Parameters of the fit()-Method:
{
"epochs": 1,
"evaluation_steps": 440,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 0.00032
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 441,
"weight_decay": 0.01
}
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: BloomModel
(1): Pooling({'word_embedding_dimension': 2048, '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})
)