模型:
bstds/labse_schema_matcher
这是一个 sentence-transformers 模型:它将句子和段落映射到一个768维的稠密向量空间中,可用于聚类或语义搜索等任务。
如果已经安装了 sentence-transformers ,那么使用该模型变得很简单:
pip install -U sentence-transformers
然后可以像这样使用该模型:
from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings)
如果没有 sentence-transformers ,可以像这样使用该模型:首先,通过变换器模型传递输入,然后必须在上下文化的单词嵌入之上应用正确的汇集操作。
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('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # 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)
对于这个模型的自动评估,请参见 Sentence Embeddings Benchmark : https://seb.sbert.net
该模型训练的参数为:
DataLoader :
torch.utils.data.dataloader.DataLoader 长度为3814,具有以下参数:
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
Loss :
sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss 具有以下参数:
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
fit() 方法的参数:
{ "epochs": 30, "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": 11442, "weight_decay": 0.01 }
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}) )