sentence-bert-base-italian-xxl-cased

这是一个模型：它将句子和段落映射到一个768维的稠密向量空间，并可用于聚类或语义搜索等任务。它派生自模型 dbmdz/bert-base-italian-xxl-uncased ，请查看其模型卡以获取更多信息。

使用（Sentence-Transformers）

当您安装了模型 sentence-transformers 后，使用该模型变得很简单：

pip install -U sentence-transformers

然后，您可以像这样使用该模型：

from sentence_transformers import SentenceTransformer
sentences = ["Una ragazza si acconcia i capelli.", "Una ragazza si sta spazzolando i capelli."]

model = SentenceTransformer('nickprock/sentence-bert-base-italian-xxl-uncased')
embeddings = model.encode(sentences)
print(embeddings)

使用（HuggingFace Transformers）

如果没有模型 sentence-transformers ，您可以这样使用该模型：首先，将输入传递给转换模型，然后必须在上下文化的词嵌入之上应用正确的汇聚操作。

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 = ['Una ragazza si acconcia i capelli.', 'Una ragazza si sta spazzolando i capelli.']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('nickprock/sentence-bert-base-italian-xxl-uncased')
model = AutoModel.from_pretrained('nickprock/sentence-bert-base-italian-xxl-uncased')

# 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)

评估结果

有关对该模型的自动评估，请参阅句子嵌入基准 https://seb.sbert.net 。

训练

模型是使用以下参数进行训练的：

DataLoader：

torch.utils.data.dataloader.DataLoader，长度为360，参数如下：

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

损失：

sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss

fit()方法的参数：

{
    "epochs": 10,
    "evaluation_steps": 500,
    "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": 1500,
    "warmup_steps": 360,
    "weight_decay": 0.01
}

完整的模型架构

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})
)