未大小写的芬兰语句子BERT模型

从FinBERT训练的芬兰语句子BERT模型。使用 the cased model 检索400亿个句子数据集中最相似的句子的演示可以在此找到 here 。

训练

• 库： sentence-transformers
• FinBERT模型：TurkuNLP/bert-base-finnish-uncased-v1
• 数据：所提供的数据 here ，包括芬兰语释义语料库和自动收集的释义候选句子（500K个正向和5M个负向）
• 汇聚方式：均值汇聚
• 任务：二元预测，判断两个句子是否是释义句子。注意：标签3和4被视为释义，标签1和2被视为非释义 Details on labels

用法

与 HuggingFace documentation 相同。可以通过SentenceTransformer或HuggingFace Transformers实现。

SentenceTransformer

from sentence_transformers import SentenceTransformer
sentences = ["Tämä on esimerkkilause.", "Tämä on toinen lause."]

model = SentenceTransformer('TurkuNLP/sbert-uncased-finnish-paraphrase')
embeddings = model.encode(sentences)
print(embeddings)

HuggingFace Transformers

from transformers import AutoTokenizer, AutoModel
import torch


# 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 = ["Tämä on esimerkkilause.", "Tämä on toinen lause."]

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('TurkuNLP/sbert-uncased-finnish-paraphrase')
model = AutoModel.from_pretrained('TurkuNLP/sbert-uncased-finnish-paraphrase')

# 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'])

print("Sentence embeddings:")
print(sentence_embeddings)

评估结果

评估结果的出版物正在草拟中。

完整模型架构

SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': True}) 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})
    )

引用及作者

在出版物草拟完成之前，请引用 this page 。

参考资料

• J. Kanerva, F. Ginter, LH. Chang, I. Rastas, V. Skantsi, J. Kilpeläinen, HM. Kupari, J. Saarni, M. Sevón, and O. Tarkka. Finnish Paraphrase Corpus. In NoDaLiDa 2021, 2021.
• N. Reimers and I. Gurevych. Sentence-BERT: Sentence embeddings using Siamese BERT-networks. In EMNLP-IJCNLP, pages 3982–3992, 2019.
• A. Virtanen, J. Kanerva, R. Ilo, J. Luoma, J. Luotolahti, T. Salakoski, F. Ginter, and S. Pyysalo. Multilingual is not enough: BERT for Finnish. arXiv preprint arXiv:1912.07076, 2019.