Model reference · open weights
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 by | Fjoralb1 |
|---|---|
| Type | Embedding models |
| Task | Embeddings |
| Parameters (lead) | 118M |
| Context | 512 tokens |
| Runs with | sentence-transformers |
| Released | 2024-03-12 |
| Popularity | 3k downloads / month |
| Licence | Unknown |
About
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.
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)
For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net
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
}
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'})
)
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
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.