🚀 pierluigic/xl-lexeme
該模型基於 sentence-transformers 構建,它能將句子中的目標詞映射到一個 1024 維的密集向量空間,可用於聚類或語義搜索等任務。
🚀 快速開始
本模型可用於將句子中的目標詞映射到 1024 維的密集向量空間,適用於聚類、語義搜索等任務。
✨ 主要特性
- 基於 sentence-transformers 構建。
- 可將句子中的目標詞映射到 1024 維的密集向量空間。
- 適用於聚類或語義搜索等任務。
📦 安裝指南
安裝相關庫:
git clone git@github.com:pierluigic/xl-lexeme.git
cd xl-lexeme
pip3 install .
💻 使用示例
基礎用法
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)
🔧 技術細節
訓練參數
該模型使用以下參數進行訓練:
數據加載器:
torch.utils.data.dataloader.DataLoader
,長度為 16531,參數如下:
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
損失函數:
sentence_transformers.losses.ContrastiveLoss.ContrastiveLoss
,參數如下:
{'distance_metric': 'SiameseDistanceMetric.COSINE_DISTANCE', 'margin': 0.5, 'size_average': True}
fit()
方法的參數:
{
"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
}
完整模型架構
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})
)
📄 引用信息
@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"
}