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,可用于聚类或语义搜索等任务。
当前版本是在私有语料库上进行的 LaBSE 模型的蒸馏。
当您安装了 sentence-transformers 后,使用此模型变得很容易:
pip install -U sentence-transformers
然后您可以像这样使用该模型:
from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim sentences = [ "הם היו שמחים לראות את האירוע שהתקיים.", "לראות את האירוע שהתקיים היה מאוד משמח להם." ] model = SentenceTransformer('imvladikon/sentence-transformers-alephbert') embeddings = model.encode(sentences) print(cos_sim(*tuple(embeddings)).item()) # 0.883316159248352
如果没有 sentence-transformers ,您可以像这样使用该模型:首先,通过变换器模型进行输入,然后必须在上下文化的词嵌入之上应用正确的汇集操作。
import torch from torch import nn from transformers import AutoTokenizer, AutoModel #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 = [ "הם היו שמחים לראות את האירוע שהתקיים.", "לראות את האירוע שהתקיים היה מאוד משמח להם." ] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('imvladikon/sentence-transformers-alephbert') model = AutoModel.from_pretrained('imvladikon/sentence-transformers-alephbert') # 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']) cos_sim = nn.CosineSimilarity(dim=0, eps=1e-6) print(cos_sim(sentence_embeddings[0], sentence_embeddings[1]).item())
有关此模型的自动评估,请参见Sentence Embeddings Benchmark: https://seb.sbert.net
该模型训练时使用了以下参数:
DataLoader:
torch.utils.data.dataloader.DataLoader长度为44999,参数为:
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
Loss:
sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss参数为:
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
fit()方法的参数:
{ "epochs": 10, "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": 44999, "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}) )
@misc{seker2021alephberta, title={AlephBERT:A Hebrew Large Pre-Trained Language Model to Start-off your Hebrew NLP Application With}, author={Amit Seker and Elron Bandel and Dan Bareket and Idan Brusilovsky and Refael Shaked Greenfeld and Reut Tsarfaty}, year={2021}, eprint={2104.04052}, archivePrefix={arXiv}, primaryClass={cs.CL} }
@misc{reimers2019sentencebert, title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks}, author={Nils Reimers and Iryna Gurevych}, year={2019}, eprint={1908.10084}, archivePrefix={arXiv}, primaryClass={cs.CL} }