Model reference · open weights
xl-lexeme is an open-weight embedding model from pierluigic. 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 | pierluigic |
|---|---|
| Type | Embedding models |
| Task | Embeddings |
| Context | 514 tokens |
| Runs with | sentence-transformers |
| Released | 2023-05-14 |
| Popularity | 515 downloads / month |
| Licence | Unknown |
About
This model is based on sentence-transformers: It maps target word in sentences to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Install the library:
git clone git@github.com:pierluigic/xl-lexeme.git
cd xl-lexeme
pip3 install .
Then you can use the model like this:
from WordTransformer import WordTransformer, InputExample
model = WordTransformer('pierluigic/xl-lexeme')
examples = InputExample(texts="the quick fox jumps over the lazy dog", positions=[10,13])
fox_embedding = model.encode(examples) #The embedding of the target word "fox"
The model was trained with the parameters:
DataLoader:
torch.utils.data.dataloader.DataLoader of length 16531 with parameters:
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
Loss:
sentence_transformers.losses.ContrastiveLoss.ContrastiveLoss with parameters:
{'distance_metric': 'SiameseDistanceMetric.COSINE_DISTANCE', 'margin': 0.5, 'size_average': True}
Parameters of the fit()-Method:
{
"epochs": 10,
"evaluation_steps": 4132,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "",
"optimizer_params": {
"lr": 1e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 16531.0,
"weight_decay": 0.0
}
SentenceTransformerTarget(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(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})
)
@inproceedings{cassotti-etal-2023-xl,
title = "{XL}-{LEXEME}: {W}i{C} Pretrained Model for Cross-Lingual {LEX}ical s{EM}antic chang{E}",
author = "Cassotti, Pierluigi and
Siciliani, Lucia and
DeGemmis, Marco and
Semeraro, Giovanni and
Basile, Pierpaolo",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-short.135",
pages = "1577--1585"
}
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys xl-lexeme for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (xl-lexeme 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":"xl-lexeme","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.