Contradiction Psb

🚀 contradiction-psb

這是一個使用PatentSBERTa識別專利中矛盾句子的模型。它屬於 sentence-transformers 模型，可將句子和段落映射到768維的密集向量空間，適用於聚類或語義搜索等任務。

🚀 快速開始

本模型的使用依賴於 sentence-transformers 庫，你可以按照以下步驟開始使用：

安裝依賴

pip install -U sentence-transformers

使用示例

基礎用法

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

model = SentenceTransformer('nategro/contradiction-psb')
embeddings = model.encode(sentences)
print(embeddings)

高級用法

若未安裝 sentence-transformers，你可以按以下方式使用模型：首先將輸入傳遞給Transformer模型，然後對上下文詞嵌入應用合適的池化操作。

from transformers import AutoTokenizer, AutoModel
import torch


def cls_pooling(model_output, attention_mask):
    return model_output[0][:,0]


# 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('nategro/contradiction-psb')
model = AutoModel.from_pretrained('nategro/contradiction-psb')

# 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, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)

📚 詳細文檔

評估結果

若需對本模型進行自動評估，請參考 Sentence Embeddings Benchmark：https://seb.sbert.net

訓練信息

本模型的訓練參數如下：

數據加載器

torch.utils.data.dataloader.DataLoader，長度為496，參數如下：

{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}

損失函數

sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss

訓練方法參數

{
    "epochs": 1,
    "evaluation_steps": 0,
    "evaluator": "NoneType",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": 496,
    "warmup_steps": 50,
    "weight_decay": 0.01
}

完整模型架構

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)

引用與作者

本模型使用了預訓練模型：AI-Growth-Lab/PatentSBERTa