社区中SentenceTransformers模型大多是通过句子相似度任务得到的。下面是载入的sentence-transformers/all-MiniLM-L6-v2模型。 fromsentence_transformersimportSentenceTransformermodel_id="sentence-transformers/all-MiniLM-L6-v2"model=SentenceTransformer(model_id) 接下来是最重要的部分:数据格式 如何准备训练SentenceTransform...
请问大佬你解决这个问题了吗？
# 使用GPU(如果可用) model_name = 'all-MiniLM-L6-v2' model = SentenceTransformer(model_name, device='cuda' if torch.cuda.is_available() else 'cpu') # 示例句子 sentences = ['This is an example sentence.', 'Each sentence is converted.'] # 计算嵌入 embeddings = model.encode(sentences) ...
from sentence_transformers import SentenceTransformer # 创建多进程池 model = SentenceTransformer('all-MiniLM-L6-v2') pool = model.start_multi_process_pool() # 使用多进程池进行并行推理 sentences = ["This is sentence {}".format(i) for i in range(100000)] embeddings = model.encode_multi_proce...
model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2") # 句子token化 encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=128, return_tensors='pt') 拿到模型的输出 with torch.no_grad():
WikiGameBot utilizes HuggingFace's SentenceTransformer model, specifically 'sentence-transformers/all-MiniLM-L6-v2,' for generating embeddings of Wikipedia summaries. This model is employed to represent Wikipedia page content as high-dimensional vectors....
corpus when using the all-MiniLM-L6-v2 model (depending on the abstract’s length), about 4 to 7 s when using the all-MiniLM-L12-v2 model, and up to about 30–50 s when using the all-mpnet-base-v2 model, whose embeddings are twice as long as the ones of the first two models....
As can be seen, Model2Vec models outperform the GloVe and WL256 models on all classification tasks, and are competitive with the all-MiniLM-L6-v2 model, while being much faster.The figure below shows the relationship between the number of sentences per second and the average classification ...
python3 examples/sample_dylib.py models/all-MiniLM-L6-v2/ggml-model-f16.bin#bert_load_from_file: loading model from '../models/all-MiniLM-L6-v2/ggml-model-f16.bin' - please wait ...#bert_load_from_file: n_vocab = 30522#bert_load_from_file: n_max_tokens = 512#bert_load_from...
这个问题主要是由变压器版本引起的。我建议安装变压器==4.32.0或更高版本。