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1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": true,
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+ }
2_Dense/config.json ADDED
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+ {"in_features": 768, "out_features": 768, "bias": true, "activation_function": "torch.nn.modules.activation.Tanh"}
2_Dense/pytorch_model.bin ADDED
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README.md ADDED
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+ ---
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+ library_name: sentence-transformers
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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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+ datasets:
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+ - embedding-data/QQP_triplets
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+ ---
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+
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+ # pranay-j/quora-query-aligner-matryoshka
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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+
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+ <!--- Describe your model here -->
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+
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+ ## Usage (Sentence-Transformers)
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+
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+ Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
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+
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+ ```
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+ pip install -U sentence-transformers
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+ ```
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+
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+ Then you can use the model like this:
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+
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+ ```python
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+ from sentence_transformers import SentenceTransformer
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+ sentences = ["This is an example sentence", "Each sentence is converted"]
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+
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+ model = SentenceTransformer('pranay-j/quora-query-aligner-matryoshka')
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+ embeddings = model.encode(sentences)
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+ print(embeddings)
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+ ```
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+
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+
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+
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+ ## Evaluation Results
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+
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+ <!--- Describe how your model was evaluated -->
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+
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+ For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=pranay-j/quora-query-aligner-matryoshka)
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+
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+
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+ ## Training
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+ The model was trained with the parameters:
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+
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+ **DataLoader**:
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+
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+ `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 2226 with parameters:
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+ ```
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+ {'batch_size': 32}
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+ ```
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+
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+ **Loss**:
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+
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+ `sentence_transformers.losses.MatryoshkaLoss.MatryoshkaLoss` with parameters:
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+ ```
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+ {'loss': 'MultipleNegativesRankingLoss', 'matryoshka_dims': [768, 512, 256, 128, 64], 'matryoshka_weights': [1, 1, 1, 1, 1], 'n_dims_per_step': -1}
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+ ```
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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": 3,
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+ "evaluation_steps": 0,
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+ "evaluator": "NoneType",
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+ "max_grad_norm": 1,
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+ "optimizer_class": "<class 'torch.optim.adamw.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": 668,
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+ "weight_decay": 0.01
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+ }
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+ ```
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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': 512, 'do_lower_case': False}) with Transformer model: BertModel
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+ (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
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+ (2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
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+ )
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+ ```
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+
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+ ## Citing & Authors
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+
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+ <!--- Describe where people can find more information -->
config.json ADDED
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+ }
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