🚀 mmarco-sentence-flare-it

这是一个 sentence-transformers 模型，它可以将句子和段落映射到一个 384 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

安装依赖

使用此模型，你需要安装 sentence-transformers：

pip install -U sentence-transformers

✨ 主要特性

• 能够将句子和段落映射到 384 维的密集向量空间。
• 可用于聚类、语义搜索等任务。

📦 安装指南

若要使用该模型，你需要安装 sentence-transformers，安装命令如下：

pip install -U sentence-transformers

💻 使用示例

基础用法（Sentence-Transformers）

from sentence_transformers import SentenceTransformer, util

query = "Quante persone vivono a Londra?"
docs = ["A Londra vivono circa 9 milioni di persone", "Londra è conosciuta per il suo quartiere finanziario"]

#Load the model
model = SentenceTransformer('nickprock/mmarco-sentence-flare-it')

#Encode query and documents
query_emb = model.encode(query)
doc_emb = model.encode(docs)

#Compute dot score between query and all document embeddings
scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist()

#Combine docs & scores
doc_score_pairs = list(zip(docs, scores))

#Sort by decreasing score
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)

#Output passages & scores
for doc, score in doc_score_pairs:
    print(score, doc)

高级用法（HuggingFace Transformers）

不使用 sentence-transformers 时，你可以按以下方式使用该模型：首先，将输入传递给 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

#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output.last_hidden_state
    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)


#Encode text
def encode(texts):
    # Tokenize sentences
    encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt')

    # Compute token embeddings
    with torch.no_grad():
        model_output = model(**encoded_input, return_dict=True)

    # Perform pooling
    embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

    return embeddings


# Sentences we want sentence embeddings for
query = "Quante persone vivono a Londra?"
docs = ["A Londra vivono circa 9 milioni di persone", "Londra è conosciuta per il suo quartiere finanziario"]

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained("nickprock/mmarco-sentence-flare-it")
model = AutoModel.from_pretrained("nickprock/mmarco-sentence-flare-it")

#Encode query and docs
query_emb = encode(query)
doc_emb = encode(docs)

#Compute dot score between query and all document embeddings
scores = torch.mm(query_emb, doc_emb.transpose(0, 1))[0].cpu().tolist()

#Combine docs & scores
doc_score_pairs = list(zip(docs, scores))

#Sort by decreasing score
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)

#Output passages & scores
print("Query:", query)
for doc, score in doc_score_pairs:
    print(score, doc)

📚 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

训练参数

该模型使用以下参数进行训练：

数据加载器

torch.utils.data.dataloader.DataLoader，长度为 7500，参数如下：

{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}

损失函数

sentence_transformers.losses.TripletLoss.TripletLoss，参数如下：

{'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5}

fit() 方法的参数

{
    "epochs": 10,
    "evaluation_steps": 500,
    "evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": 1500,
    "warmup_steps": 7500,
    "weight_decay": 0.01
}

完整模型架构

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 384, '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

有关 基础模型的更多信息

📄 许可证

本项目采用 Apache-2.0 许可证。

模型信息