模型:
m3hrdadfi/roberta-zwnj-wnli-mean-tokens
当你已经安装了 sentence-transformers ,使用这个模型变得很容易:
pip install -U sentence-transformers
然后你可以像这样使用模型:
from sentence_transformers import SentenceTransformer sentences = [ 'اولین حکمران شهر بابل کی بود؟', 'در فصل زمستان چه اتفاقی افتاد؟', 'میراث کوروش' ] model = SentenceTransformer('m3hrdadfi/roberta-zwnj-wnli-mean-tokens') embeddings = model.encode(sentences) print(embeddings)
如果没有安装 sentence-transformers ,你可以像这样使用模型:首先,将输入传递给变换器模型,然后必须在上下文化的词嵌入之上应用正确的池化操作。
from transformers import AutoTokenizer, AutoModel import torch # Max Pooling - Take the max value over time for every dimension. def max_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() token_embeddings[input_mask_expanded == 0] = -1e9 # Set padding tokens to large negative value return torch.mean(token_embeddings, 1)[0] # Sentences we want sentence embeddings for sentences = [ 'اولین حکمران شهر بابل کی بود؟', 'در فصل زمستان چه اتفاقی افتاد؟', 'میراث کوروش' ] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('m3hrdadfi/roberta-zwnj-wnli-mean-tokens') model = AutoModel.from_pretrained('m3hrdadfi/roberta-zwnj-wnli-mean-tokens') # 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, max pooling. sentence_embeddings = max_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings)
请在 HERE 上发布一个Github问题。