Mmarco Bert Base Italian Uncased

🚀 MMARCO-bert-base-italian-uncased

這是一個 sentence-transformers 模型，它可以將句子和段落映射到 768 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

安裝依賴

使用此模型，你需要安裝 sentence-transformers：

pip install -U sentence-transformers

使用示例

基礎用法（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-bert-base-italian-uncased')

#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)

高級用法（HuggingFace Transformers）

若未安裝 sentence-transformers，你可以這樣使用該模型：首先，將輸入傳遞給 Transformer 模型，然後對上下文詞嵌入應用正確的池化操作。

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-bert-base-italian-uncased")
model = AutoModel.from_pretrained("nickprock/mmarco-bert-base-italian-uncased")

#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)

✨ 主要特性

• 能夠將句子和段落映射到 768 維的密集向量空間。
• 可用於聚類或語義搜索等任務。

📚 詳細文檔

評估結果

要對該模型進行自動評估，請參考 Sentence Embeddings Benchmark：https://seb.sbert.net

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 6250，參數如下：

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

損失函數

sentence_transformers.losses.TripletLoss.TripletLoss，參數如下：

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

fit() 方法的參數

{
    "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": 6250,
    "weight_decay": 0.01
}

完整模型架構

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, '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})
)

🔧 技術細節

模型指標

📄 許可證

該模型採用 MIT 許可證。