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.
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
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'})
)