对于大规模数据处理,可以使用sentencetransformer提供的多进程池功能进行并行计算: python 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...
to start only one process per GPU. This method works together with encode_multi_process and stop_multi_process_pool. :param target_devices: PyTorch target devices, e.g. ["cuda:0", "cuda:1", ...] or ["cpu", "cpu", "cpu", "cpu"]. ...
sentence_transformers cross_encoder datasets evaluation losses models readers LoggingHandler.py SentenceTransformer.py __init__.py backend.py data_collator.py fit_mixin.py model_card.py model_card_template.md model_card_templates.py peft_mixin.py ...
In this post, we looked at sentenceTransformer library and paper and we saw how it addresses the problem of computing sentence embedding from BERT. SentenceTransformer fine-tune BERT on three sentence related dataset namely NLI, STS and triplet datasets in a siamese and triplet architecture to en...
CrossEncoder models are often even better than biencoder (SentenceTransformer) models, as the model can compare two texts using the attention mechanism, unlike biencoders. However, they are more computationally expensive as well. They are commonly used for reranking the top retrieval results of a...
1,基于Bayesian Theory,融Hard Attention、Soft Attention、Self-Attention、Multi-head Attention于一身的Transformer架构 2,为什么说抛弃了传统模型(例如RNN、 LSTM、CNN等)的Transformer拉开了非序列化模型时代的序幕? 3,为什么说Transformer是预训练领域底层通用引擎? 4,Transformer的Input-Encoder-Decoder-Output模型组建...
BitFit: Simple parameter-efficient fine-tuning for transformer-based masked language-models Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Association for Computational Linguistics, Dublin, Ireland (2022), pp. 1-9 CrossrefView in Scop...
A: This is often due to the misuse of BertClient in multi-thread/process environment. Note that you can’t reuse one BertClient among multiple threads/processes, you have to make a separate instance for each thread/process. For example, the following won't work at all:...
A: This is often due to the misuse of BertClient in multi-thread/process environment. Note that you can’t reuse one BertClient among multiple threads/processes, you have to make a separate instance for each thread/process. For example, the following won't work at all: # BAD example bc...
A: This is often due to the misuse of BertClient in multi-thread/process environment. Note that you can’t reuse one BertClient among multiple threads/processes, you have to make a separate instance for each thread/process. For example, the following won't work at all:...