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metadata.tsv

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编辑模型卡片
all-MiniLM-L6-v2
这是一个 sentence-transformers 模型：它将句子和段落映射到 384 维的密集向量空间中，可用于聚类或语义搜索等任务。
使用（Sentence-Transformers）
当你安装了 sentence-transformers 后，使用这个模型就变得很容易：
pip install -U sentence-transformers
然后你可以像这样使用模型：
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
embeddings = model.encode(sentences)
print(embeddings)
使用（HuggingFace Transformers）
如果没有 sentence-transformers，你可以这样使用模型：首先，你将输入通过变换器模型，然后你需要在上下文化的词嵌入之上应用正确的池化操作。
from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn.functional as F
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
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 we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
# Normalize embeddings
sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:")
print(sentence_embeddings)
背景
该项目旨在使用自监督对比学习目标，在非常大的句子级数据集上训练句子嵌入模型。我们使用了预训练的 nreimers/MiniLM-L6-H384-uncased 模型，并在 1B 句子对数据集上进行了微调。我们使用对比学习目标：给定句子对中的一个句子，模型应该预测在一组随机抽样的其他句子中，哪个句子实际上在我们的数据集中与之配对。
我们在开发这个模型期间 使用 JAX/Flax 进行 NLP 和 CV 的社区周 由 Hugging Face 组织。我们开发了这个模型作为项目的一部分： 使用 10 亿训练对训练有史以来最好的句子嵌入模型 。我们受益于高效的硬件基础设施来运行项目：7 个 TPU v3-8，以及来自 Google 的 Flax、JAX 和 Cloud 团队成员关于高效深度学习框架的干预。
预期用途
我们的模型旨在用作句子和短段落编码器。给定输入文本，它会输出一个捕捉语义信息的向量。句子向量可用于信息检索、聚类或句子相似性任务。
默认情况下，超过256个词片的输入文本会被截断。
训练过程
预训练
我们使用预训练的 nreimers/MiniLM-L6-H384-uncased 模型。请参阅模型卡以获取有关预训练过程的更多详细信息。
微调
我们使用对比目标来微调模型。形式上，我们计算批次中每对可能的句子对的余弦相似度。然后通过与真实对进行比较，应用交叉熵损失。
超参数
我们在 TPU v3-8 上训练了我们的模型。我们在 100k 步内使用批量大小为 1024（每个 TPU 核心 128）的方式训练模型。我们使用了 500 的学习率预热。序列长度限制为 128 个标记。我们使用了 AdamW 优化器，学习率为 2e-5。完整的训练脚本可在当前仓库中访问：train_script.py。
训练数据
我们使用多个数据集的拼接来微调我们的模型。句子对的总数超过 10 亿句。我们根据加权概率对每个数据集进行抽样，其配置详细信息在 data_config.json 文件中。
Dataset Paper Number of training tuples
Reddit 评论（2015-2018） 论文 726,484,430
S2ORC 引文对（摘要） 论文 116,288,806
WikiAnswers 重复问题对 论文 77,427,422
PAQ（问题，答案）对 论文 64,371,441
S2ORC 引文对（标题） 论文 52,603,982
S2ORC（标题，摘要） 论文 41,769,185
Stack Exchange（标题，内容）对 - 25,316,456
Stack Exchange (标题+正文，回答) 对 - 21,396,559
Stack Exchange（标题，回答）对 - 21,396,559
MS MARCO 三元组 论文 9,144,553
GOOAQ：开放式问答与多样化答案类型 论文 3,012,496
Yahoo Answers（标题，回答） 论文 1,198,260
代码搜索 - 1,151,414
COCO 图像标题 论文 828,395
SPECTER 引文三元组 论文 684,100
Yahoo Answers（问题，回答） 论文 681,164
Yahoo Answers（标题，问答） 论文 659,896
SearchQA 论文 582,261
用简单的话解释 论文 325,475
Flickr 30k 论文 317,695
Stack Exchange 重复问题（标题）  304,525
AllNLI (SNLI 和 MultiNLI 论文 SNLI, 论文 MultiNLI 277,230
Stack Exchange 重复问题（正文）  250,519
Stack Exchange 重复问题（标题+正文）  250,460
句子压缩 论文 180,000
维基百科 论文 128,542
Altlex 论文 112,696
Quora 问题三元组 - 103,663
简单维基百科 论文 102,225
自然问题 (NQ) 论文 100,231
SQuAD2.0 论文 87,599
TriviaQA - 73,346
总共  1,170,060,424

tensor.tsv

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