🚀 pritamdeka/BioBERT-mnli-snli-scinli-scitail-mednli-stsb
這是一個 sentence-transformers 模型,它能將句子和段落映射到一個 768 維的密集向量空間,可用於聚類或語義搜索等任務。該模型在 SNLI、MNLI、SCINLI、SCITAIL、MEDNLI 和 STSB 數據集上進行了訓練,以提供強大的句子嵌入。
🚀 快速開始
📦 安裝指南
若你已安裝 sentence-transformers,使用此模型將變得十分簡單:
pip install -U sentence-transformers
💻 使用示例
基礎用法(Sentence-Transformers)
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('pritamdeka/BioBERT-mnli-snli-scinli-scitail-mednli-stsb')
embeddings = model.encode(sentences)
print(embeddings)
基礎用法(HuggingFace Transformers)
若未安裝 sentence-transformers,你可以按以下方式使用該模型:首先,將輸入傳遞給 transformer 模型,然後對上下文詞嵌入應用正確的池化操作。
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('pritamdeka/BioBERT-mnli-snli-scinli-scitail-mednli-stsb')
model = AutoModel.from_pretrained('pritamdeka/BioBERT-mnli-snli-scinli-scitail-mednli-stsb')
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)
📚 詳細文檔
評估結果
若要對該模型進行自動評估,請參閱 Sentence Embeddings Benchmark:https://seb.sbert.net
訓練
該模型使用以下參數進行訓練:
數據加載器(DataLoader):
torch.utils.data.dataloader.DataLoader
,長度為 90,參數如下:
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
損失函數(Loss):
sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss
fit()
方法的參數:
{
"epochs": 4,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 36,
"weight_decay": 0.01
}
完整模型架構
SentenceTransformer(
(0): Transformer({'max_seq_length': 100, 'do_lower_case': False}) 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})
)
引用與作者
如果你使用了該模型,請引用以下文獻:
@inproceedings{deka2022evidence,
title={Evidence Extraction to Validate Medical Claims in Fake News Detection},
author={Deka, Pritam and Jurek-Loughrey, Anna and others},
booktitle={International Conference on Health Information Science},
pages={3--15},
year={2022},
organization={Springer}
}