Aiky Sentence Bertino

🚀 aiky-sentence-bertino模型

aiky-sentence-bertino是一個基於sentence-transformers的模型，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、語義搜索等任務。

🚀 快速開始

📦 安裝指南

若要使用此模型，需要安裝sentence-transformers，可以使用以下命令進行安裝：

pip install -U sentence-transformers

💻 使用示例

基礎用法

使用sentence-transformers庫調用該模型的示例代碼如下：

from sentence_transformers import SentenceTransformer
sentences = ["Questa è una frase di esempio", "Ogni frase viene convertita"]

model = SentenceTransformer('aiknowyou/aiky-sentence-bertino')
embeddings = model.encode(sentences)
print(embeddings)

高級用法

若不使用sentence-transformers庫，也可以直接使用transformers庫調用該模型。需要先將輸入數據通過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[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('aiknowyou/aiky-sentence-bertino')
model = AutoModel.from_pretrained('aiknowyou/aiky-sentence-bertino')

# 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. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)

📚 詳細文檔

評估結果

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

訓練細節

該模型的訓練參數如下：

數據加載器

使用torch.utils.data.dataloader.DataLoader，長度為391，參數如下：

{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}

損失函數

使用sentence_transformers.losses.OnlineContrastiveLoss.OnlineContrastiveLoss損失函數。

訓練方法參數

fit()方法的參數如下：

{
    "epochs": 20,
    "evaluation_steps": 0,
    "evaluator": "NoneType",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 1000,
    "weight_decay": 0.01
}

完整模型架構

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel 
  (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})
)

📄 許可證

本作品採用知識共享署名 - 非商業性使用 - 相同方式共享 4.0 國際許可協議進行許可。