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---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- word-similarity
- transformers
widget:
- source_sentence: "Provide a large table; this is a horizontal <t>plane</t>, and will represent the ground plane, viz."
sentences:
- "The President's <t>plane</t> landed at Goose Bay at 9:03 p.m."
- "any line joining two points on a <t>plane</t> lies wholly on that plane"
- "the flight was delayed due to trouble with the <t>plane</t>"
example_title: "plane (en)"
- source_sentence: "La <t>radice</t> del problema non è nota"
sentences:
- "il liquore è fatto dalle <t>radici</t> di liquirizia"
- "La <t>radice</t> di 2 è 4."
- "occorre pertanto trasformare la società alla <t>radice</t>"
example_title: "radice (it)"
---
# pierluigic/xl-lexeme
This model is based on [sentence-transformers](https://www.SBERT.net): It maps target word in sentences to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (WordTransformer)
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:
```python
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"
```
## Training
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": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 1e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 16531.0,
"weight_decay": 0.0
}
```
## Full Model Architecture
```
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})
)
```
## Citing & Authors
```
@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"
}
``` |