Stpushtohub Test2

🚀 NimaBoscarino/STPushToHub-test2

這是一個 句子轉換器（sentence-transformers） 模型，它能將句子和段落映射到一個 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

本模型可以通過兩種方式使用，下面將分別介紹使用 sentence-transformers 庫和 HuggingFace Transformers 庫的方法。

📦 安裝指南

若要使用 sentence-transformers 庫，需先安裝它：

pip install -U sentence-transformers

💻 使用示例

基礎用法

使用 sentence-transformers 庫

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('NimaBoscarino/STPushToHub-test2')
embeddings = model.encode(sentences)
print(embeddings)

使用 HuggingFace Transformers 庫

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('NimaBoscarino/STPushToHub-test2')
model = AutoModel.from_pretrained('NimaBoscarino/STPushToHub-test2')

# 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化評估，請參考 句子嵌入基準測試（Sentence Embeddings Benchmark）：https://seb.sbert.net

訓練

該模型使用以下參數進行訓練：

數據加載器（DataLoader）： torch.utils.data.dataloader.DataLoader，長度為 360，參數如下：

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

損失函數（Loss）： sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss

fit() 方法的參數：

{
    "epochs": 4,
    "evaluation_steps": 1000,
    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 144,
    "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})
)

🔧 技術細節

模型標籤

引用與作者

文檔中未提及引用和作者的相關信息，若需更多信息，可進一步探索。