bert-zwnj-wnli-mean-tokens句子嵌入

使用方法（Sentence-Transformers）

在安装了 sentence-transformers 后，使用这个模型非常简单：

pip install -U sentence-transformers

然后你可以这样使用模型：

from sentence_transformers import SentenceTransformer


sentences = [
    'اولین حکمران شهر بابل کی بود؟',
    'در فصل زمستان چه اتفاقی افتاد؟',
    'میراث کوروش'
]
model = SentenceTransformer('m3hrdadfi/bert-zwnj-wnli-mean-tokens')
embeddings = model.encode(sentences)
print(embeddings)

使用方法（HuggingFace Transformers）

在没有 sentence-transformers 的情况下，你可以这样使用模型：首先，将输入传递给变换器模型，然后在上下文化的词嵌入上应用合适的池化操作。

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/bert-zwnj-wnli-mean-tokens')
model = AutoModel.from_pretrained('m3hrdadfi/bert-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)

有问题吗？

请在 HERE 上发布Github问题。