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pip install -U sentence-transformers1from sentence_transformers import SentenceTransformer
2sentences = ["This is an example sentence", "Each sentence is converted"]
3
4model = SentenceTransformer('Wissam42/paraphrase-multilingual-mpnet-base-v2-retuned-fr')
5embeddings = model.encode(sentences)
6print(embeddings)1from transformers import AutoTokenizer, AutoModel
2import torch
3
4
5#Mean Pooling - Take attention mask into account for correct averaging
6def mean_pooling(model_output, attention_mask):
7 token_embeddings = model_output[0] #First element of model_output contains all token embeddings
8 input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
9 return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
10
11
12# Sentences we want sentence embeddings for
13sentences = ['This is an example sentence', 'Each sentence is converted']
14
15# Load model from HuggingFace Hub
16tokenizer = AutoTokenizer.from_pretrained('Wissam42/paraphrase-multilingual-mpnet-base-v2-retuned-fr')
17model = AutoModel.from_pretrained('Wissam42/paraphrase-multilingual-mpnet-base-v2-retuned-fr')
18
19# Tokenize sentences
20encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
21
22# Compute token embeddings
23with torch.no_grad():
24 model_output = model(**encoded_input)
25
26# Perform pooling. In this case, mean pooling.
27sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
28
29print("Sentence embeddings:")
30print(sentence_embeddings)torch.utils.data.dataloader.DataLoader of length 360 with parameters:{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}__main__.CosineSimilarityLoss{
"epochs": 10,
"evaluation_steps": 500,
"evaluator": "__main__.CustomEmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"eps": 1e-06,
"lr": 1e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 360,
"weight_decay": 0.01
}SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)