đ {MODEL_NAME}
This is a sentence-transformers model that maps sentences and paragraphs to a 768-dimensional dense vector space. It can be used for tasks such as clustering or semantic search.
đ Quick Start
This model is a sentence-transformers model. It maps sentences and paragraphs to a 768-dimensional dense vector space and can be used for tasks like clustering or semantic search.
⨠Features
- Multilingual Support: Supports languages such as Ukrainian (
uk
), English (en
), Polish (pl
), and Russian (ru
).
- Updated Version: It is an updated version of the uaritm/multilingual_en_ru_uk model.
- Specific Use Case: Used in the multilingual analysis of patient complaints on the Virtual General Practice platform to determine the required doctor specialty.
đĻ Installation
Using this model becomes easy when you have sentence-transformers installed:
pip install -U sentence-transformers
đģ Usage Examples
Basic Usage
If you have sentence-transformers installed:
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)
Advanced Usage
Without sentence-transformers, you can use the model as follows. First, pass your input through the transformer model, then apply the appropriate pooling operation on top of the contextualized word embeddings.
from transformers import AutoTokenizer, AutoModel
import torch
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
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 = ['This is an example sentence', 'Each sentence is converted']
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
model_output = model(**encoded_input)
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
đ Documentation
Evaluation Results
For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net
Training
The model was trained with the following parameters:
DataLoader:
torch.utils.data.dataloader.DataLoader
of length 50184 with parameters:
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
Loss:
sentence_transformers.losses.MSELoss.MSELoss
Parameters of the fit()-Method:
{
"epochs": 4,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"eps": 1e-06,
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
Full Model Architecture
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})
)
đ License
This model is licensed under the apache-2.0
license.
đ Citing & Authors
@misc{Uaritm,
title={sentence-transformers: Semantic similarity of medical texts},
author={Vitaliy Ostashko},
year={2023},
url={https://aihealth.site},
}
Information Table
Property |
Details |
Pipeline Tag |
sentence-similarity |
Tags |
sentence-transformers, feature-extraction, sentence-similarity, transformers - multilingual - en - ru - uk - pl |
License |
apache-2.0 |
Datasets |
Helsinki-NLP/tatoeba_mt |
Metrics |
mse |
Languages |
uk, en, pl, ru |
Library Name |
sentence-transformers |