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--- |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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--- |
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# SGPT-125M-mean-nli-linear5 |
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## Usage |
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For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt |
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## Evaluation Results |
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For eval results, refer to our paper: https://arxiv.org/abs/2202.08904 |
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## Training |
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The model was trained with the parameters: |
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**DataLoader**: |
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`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 8807 with parameters: |
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``` |
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{'batch_size': 64} |
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``` |
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**Loss**: |
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`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: |
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``` |
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{'scale': 20.0, 'similarity_fct': 'cos_sim'} |
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``` |
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Parameters of the fit()-Method: |
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``` |
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{ |
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"epochs": 1, |
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"evaluation_steps": 880, |
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"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", |
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"max_grad_norm": 1, |
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"optimizer_class": "<class 'transformers.optimization.AdamW'>", |
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"optimizer_params": { |
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"lr": 2e-05 |
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}, |
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"scheduler": "WarmupLinear", |
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"steps_per_epoch": null, |
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"warmup_steps": 881, |
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"weight_decay": 0.01 |
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} |
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``` |
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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': 75, 'do_lower_case': False}) with Transformer model: GPTNeoModel |
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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}) |
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(2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.GELU'}) |
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(3): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.GELU'}) |
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(4): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.GELU'}) |
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(5): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.GELU'}) |
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(6): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.GELU'}) |
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) |
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``` |
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## Citing & Authors |
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```bibtex |
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@article{muennighoff2022sgpt, |
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title={SGPT: GPT Sentence Embeddings for Semantic Search}, |
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author={Muennighoff, Niklas}, |
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journal={arXiv preprint arXiv:2202.08904}, |
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year={2022} |
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} |
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``` |
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