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Cataloged from sentence-transformers/paraphrase-multilingual-mpnet-base-v2
All models by sentence-transformers →This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
Text Embeddings Inference (TEI) is a blazing fast inference solution for text embedding models.
Send a request to /v1/embeddings to generate embeddings via the OpenAI Embeddings API:
Or check the Text Embeddings Inference API specification instead.
This model was trained by sentence-transformers.
If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:
pip install -U sentence-transformersfrom sentence_transformers import SentenceTransformersentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-mpnet-base-v2')embeddings = model.encode(sentences)print(embeddings)from transformers import AutoTokenizer, AutoModelimport torch
# Mean Pooling - Take attention mask into account for correct averagingdef 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 forsentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hubtokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-multilingual-mpnet-base-v2')model = AutoModel.from_pretrained('sentence-transformers/paraphrase-multilingual-mpnet-base-v2')
# Tokenize sentencesencoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddingswith torch.no_grad(): model_output = model(**encoded_input)
# Perform pooling. In this case, mean poolingsentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")print(sentence_embeddings)docker run -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-latest --model-id sentence-transformers/paraphrase-multilingual-mpnet-base-v2 --pooling mean --dtype float16docker run --gpus all -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cuda-latest --model-id sentence-transformers/paraphrase-multilingual-mpnet-base-v2 --pooling mean --dtype float16curl http://localhost:8080/v1/embeddings \ -H "Content-Type: application/json" \ -d '{ "model": "sentence-transformers/paraphrase-multilingual-mpnet-base-v2", "input": "This is an example sentence" }'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}))@inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084",}