🚀 {MODEL_NAME}
这是一个 sentence-transformers 模型:它可以将句子和段落映射到一个768维的密集向量空间,可用于聚类或语义搜索等任务。
🚀 快速开始
本模型可通过 sentence-transformers 或 HuggingFace Transformers 进行使用,以下为您详细介绍使用方法。
📦 安装指南
若要使用该模型,您需要安装 sentence-transformers:
pip install -U sentence-transformers
💻 使用示例
基础用法(Sentence-Transformers)
安装好 sentence-transformers
后,您可以按照以下方式使用模型:
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
高级用法(HuggingFace Transformers)
若未安装 sentence-transformers,您可以按如下方式使用模型:首先,将输入数据传入Transformer模型,然后对上下文词嵌入应用正确的池化操作。
from transformers import AutoTokenizer, AutoModel
import torch
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
sentences = ['This is an example sentence', 'Each sentence is converted']
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
model_output = model(**encoded_input)
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
📚 详细文档
评估结果
若要对该模型进行自动评估,请参考 Sentence Embeddings Benchmark:https://seb.sbert.net
训练信息
该模型使用以下参数进行训练:
数据加载器(DataLoader):
torch.utils.data.dataloader.DataLoader
,长度为13069,参数如下:
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
损失函数(Loss):
sentence_transformers.losses.ContrastiveLoss.ContrastiveLoss
,参数如下:
{'distance_metric': 'SiameseDistanceMetric.COSINE_DISTANCE', 'margin': 0.5, 'size_average': True}
fit()
方法的参数:
{
"epochs": 1,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.BinaryClassificationEvaluator.BinaryClassificationEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
完整模型架构
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
引用与作者