mxbai-embed-large-v1-nli-matryoshka

SeanLee97/mxbai-embed-large-v1-nli-matryoshka

This is a sentence-transformers model: It maps sentences & paragraphs to a 3072 dimensional dense vector space and can be used for tasks like clustering or semantic search.

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Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

Then you can use the model like this:

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('SeanLee97/mxbai-embed-large-v1-nli-matryoshka')
embeddings = model.encode(sentences)
print(embeddings)

Evaluation Results

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For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net

Training

The model was trained with the parameters:

DataLoader:

sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader of length 2201 with parameters:

{'batch_size': 256}

Loss:

sentence_transformers.losses.MatryoshkaLoss.MatryoshkaLoss with parameters:

{'loss': 'MultipleNegativesRankingLoss', 'matryoshka_dims': [3072, 2304, 1536, 768, 512, 256, 128, 64], 'matryoshka_weights': [1, 1, 1, 1, 1, 1, 1, 1], 'n_dims_per_step': -1}

Parameters of the fit()-Method:

{
    "epochs": 1,
    "evaluation_steps": 220,
    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 221,
    "weight_decay": 0.01
}

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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, 'include_prompt': True})
  (2): Dense({'in_features': 1024, 'out_features': 3072, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)

Citing & Authors

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