uaritm/multilingual_en_ru_uk

这是一个 sentence-transformers 模型：它将句子和段落映射到768维的稠密向量空间，并可用于聚类或语义搜索等任务。

这个模型的更新版本添加了波兰语，可以在这里找到： uaritm/multilingual_en_uk_pl_ru

使用方法（Sentence-Transformers）

该模型用于多语言病人投诉的多语言分析资源，以确定需要哪种医生的专长： Virtual General Practice

您可以测试模型的质量和速度

安装 sentence-transformers 后使用该模型变得很容易：

pip install -U sentence-transformers

然后您可以像这样使用模型：

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

model = SentenceTransformer('uaritm/multilingual_en_ru_uk')
embeddings = model.encode(sentences)
print(embeddings)

使用方法（HuggingFace Transformers）

如果没有 sentence-transformers ，您可以像这样使用模型：首先，将输入数据传递给变换器模型，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 = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('uaritm/multilingual_en_ru_uk')
model = AutoModel.from_pretrained('uaritm/multilingual_en_ru_uk')

# 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

对于该模型的自动评估，请参阅句子嵌入基准： https://seb.sbert.net

训练

该模型的训练参数如下：

DataLoader：

torch.utils.data.dataloader.DataLoader长度为17482，具有以下参数：

{'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}

损失：

sentence_transformers.losses.MSELoss.MSELoss

fit()方法的参数：

{
    "epochs": 15,
    "evaluation_steps": 1000,
    "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "eps": 1e-06,
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 10000,
    "weight_decay": 0.01
}

完整模型架构

SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (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})
)

引用和作者

@misc{Uaritm,
      title={sentence-transformers: Semantic similarity of medical texts}, 
      author={Vitaliy Ostashko},
      year={2022},
      url={https://aihealth.site},
}