metadata
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
pierluigic/xl-lexeme
This is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
pip install -U sentence-transformers
git clone https://github.com/pierluigic/xl-lexeme
cd xl-lexeme
Then you can use the model like this:
from WordTransformer import WordTransformer
model = WordTransformer('pierluigic/xl-lexeme')
embeddings = model.encode(sentences)
print(embeddings)
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})
)