Model reference · open weights

multilingual-e5-small-nli-matryoshka-128

Available as managed deployment Embeddings Fjoralb1 · community Embeddings 1 variants 3k dl/mo

multilingual-e5-small-nli-matryoshka-128 is an open-weight embedding model from Fjoralb1. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.

Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.

What it is

Released byFjoralb1
TypeEmbedding models
TaskEmbeddings
Parameters (lead)118M
Context512 tokens
Runs withsentence-transformers
Released2024-03-12
Popularity3k downloads / month
LicenceUnknown

About

What multilingual-e5-small-nli-matryoshka-128 is

This is a sentence-transformers model: It maps sentences & paragraphs to a 128 dimensional dense vector space and can be used for tasks like clustering or semantic search.

Read the full model card

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('Fjoralb1/multilingual-e5-small-nli-matryoshka-128')
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': [256, 128, 64, 32, 16], 'matryoshka_weights': [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": "",
    "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': 384, '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': 384, 'out_features': 128, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)

Citing & Authors

From the published model card. Full card on the HuggingFace links in the sidebar.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys multilingual-e5-small-nli-matryoshka-128 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (multilingual-e5-small-nli-matryoshka-128 below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/embeddings \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"multilingual-e5-small-nli-matryoshka-128","input":"text to embed"}'

Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.

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