Contradiction Psb Lds

🚀 矛盾專利語句識別模型 - contradiction-psb-lds

本模型藉助 PatentSBERTa 技術，專注於識別專利中的矛盾語句。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、語義搜索等任務。

🚀 快速開始

📦 安裝指南

若已安裝 sentence-transformers，使用此模型將十分便捷：

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

高級用法（HuggingFace Transformers）

若未安裝 sentence-transformers，可按以下方式使用模型：首先將輸入數據傳入 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-lds')
model = AutoModel.from_pretrained('nategro/contradiction-psb-lds')

# 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

如需對該模型進行自動化評估，請參考 Sentence Embeddings Benchmark：https://seb.sbert.net

🔧 技術細節

訓練參數

本模型的訓練參數如下： 數據加載器（DataLoader）： torch.utils.data.dataloader.DataLoader，長度為 1128，參數如下：

{'batch_size': 16, '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": 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": 1128,
    "warmup_steps": 113,
    "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

本模型使用了以下預訓練模型：AI-Growth-Lab/PatentSBERTa