MahaSBERT-STS

一种在STS数据集上微调的MahaSBERT模型（l3cube-pune/marathi-sentence-bert-nli）。这是MahaNLP项目的一部分: https://github.com/l3cube-pune/MarathiNLP 在此处共享了支持主要印度语言和跨语言句子相似度的多语言版本 indic-sentence-similarity-sbert

有关数据集、模型和基准结果的更多详细信息，请参阅我们的[论文]（ https://arxiv.org/abs/2211.11187 ）

@article{joshi2022l3cubemahasbert,
  title={L3Cube-MahaSBERT and HindSBERT: Sentence BERT Models and Benchmarking BERT Sentence Representations for Hindi and Marathi},
  author={Joshi, Ananya and Kajale, Aditi and Gadre, Janhavi and Deode, Samruddhi and Joshi, Raviraj},
  journal={arXiv preprint arXiv:2211.11187},
  year={2022}
}

monolingual Indic SBERT paper multilingual Indic SBERT paper

下面列出了其他单语相似度模型： Marathi Similarity 、 Hindi Similarity 、 Kannada Similarity 、 Telugu Similarity 、 Malayalam Similarity 、 Tamil Similarity 、 Gujarati Similarity 、 Oriya Similarity 、 Bengali Similarity 、 Punjabi Similarity 、 Indic Similarity (multilingual)

下面列出了其他单语Indic句子BERT模型： Marathi SBERT 、 Hindi SBERT 、 Kannada SBERT 、 Telugu SBERT 、 Malayalam SBERT 、 Tamil SBERT 、 Gujarati SBERT 、 Oriya SBERT 、 Bengali SBERT 、 Punjabi SBERT 、 Indic SBERT (multilingual)

这是一个 sentence-transformers 模型：它将句子和段落映射到一个768维的密集向量空间，可以用于聚类或语义搜索等任务。

用法（Sentence-Transformers）

如果您已安装 sentence-transformers ，则可以轻松使用此模型：

pip install -U sentence-transformers

然后，您可以像这样使用模型：

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)

用法（HuggingFace Transformers）

如果没有 sentence-transformers ，则可以像这样使用模型：首先，将输入通过变压器模型，然后必须在上下文化的词嵌入之上应用正确的池操作。

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 = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')

# 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)