Contradiction Detection

Models for zero-shot text classification that are up to 50 times faster than Cross-Encoders and show the same or higher accuracy.

To fine-tune a Sentence Transformer using triplet or contrastive loss, follow these steps: prepare data in the required

The Contradiction Psb Lds model is a sentence-transformers model that maps sentences and paragraphs to a 768-dimensional dense vector space, allowing for tasks like clustering or semantic search. But what does that mean for you? It means you can use this model to identify contradiction sentences in patents with ease. It's built on top of the PatentSBERTa model and can be used with either sentence-transformers or HuggingFace Transformers. The model is efficient and can be used for a variety of tasks, but have you ever wondered how it was trained? It was trained with a batch size of 16 and a learning rate of 2e-05, with a total of 1128 steps per epoch. The model's architecture is based on the MPNetModel, which is a type of transformer model. So, what makes this model unique? It's ability to identify contradiction sentences in patents, making it a valuable tool for anyone working with patent data.

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Natural language inference (NLI), also known as Recognizing Textual Entailment (RTE), is an important aspect of natural language understanding. Most research now uses machine learning and deep learning to perform this task on specific datasets, meaning their solution is not explainable nor explicit. To address the need for an explainable approach to RTE, we propose a novel pipeline that is based on translating text into an Abstract Meaning Representation (AMR) graph. For this we use a pre-trained AMR parser. We then translate the AMR graph into propositional logic and use a SAT solver for automated reasoning. In text, often commonsense suggests that an entailment (or contradiction) relationship holds between a premise and a claim, but because different wordings are used, this is not identified from their logical representations. To address this, we introduce relaxation methods to allow replacement or forgetting of some propositions. Our experimental results show this pipeline performs well on four RTE datasets.

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Contradiction retrieval refers to identifying and extracting documents that explicitly disagree with or refute the content of a query, which is important to many downstream applications like fact...

Contradiction detection is a task to recognize contradiction relations between a pair of sentences. Despite the effectiveness of traditional context-based word embedding learning algorithms in many natural language processing tasks, such algorithms are not powerful enough for contradiction detection. Contrasting words such as “overfull” and “empty” are mostly mapped into close vectors in such embedding space. To solve this problem, we develop a tailored neural network to learn contradiction-specific word embedding (CWE). The method can separate antonyms in the opposite ends of a spectrum. CWE is learned from a training corpus which is automatically generated from the paraphrase database, and is naturally applied as features to carry out contradiction detection in SemEval 2014 benchmark dataset. Experimental results show that CWE outperforms traditional context-based word embedding in contradiction detection. The proposed model for contradiction detection performs comparably with the top-performing system in accuracy of three-category classification and enhances the accuracy from 75.97% to 82.08% in the contradiction category.

Cosine similarity - the duct tape of AI. Convenient but often misused. Let's find out how to use it better.

This paper describes an efficiently scaleable approach to measuring technological similarity between patents by combining embedding techniques from na…

What are the advantages of Triplet Loss over Contrastive loss and how to efficiently implement it?

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We’re on a journey to advance and democratize artificial intelligence through open source and open science.

The release of BERT revolutionized natural language processing (NLP), setting a new standard for encoder-only transformer models. However…

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Modern updated guide on how to fine-tune BERT models for classification tasks in 2025.

If you’ve followed the news around the intersection of AI and the legal field recently, you might have heard about a lawyer who cited…

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I made a tutorial showing you how to build a contract review agentic workflow - given a vendor agreement, parse it into a set of key clauses, match it with relevant clauses from a set of guidelines (GDPR),… — Jerry Liu (@jerryjliu0)

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This study provides an efficient approach for using text data to calculate patent-to-patent (p2p) technological similarity, and presents a hybrid framework for leveraging the resulting p2p...

Various studies have focused on feature extraction methods for automatic patent classification in recent years. However, most of these approaches are based on the knowledge from experts in related domains. Here we propose a hierarchical feature extraction model (HFEM) for multi-label mechanical patent classification, which is able to capture both local features of phrases as well as global and temporal semantics. First, a n-gram feature extractor based on convolutional neural networks (CNNs) is designed to extract salient local lexical-level features. Next, a long dependency feature extraction model based on the bidirectional long–short-term memory (BiLSTM) neural network model is proposed to capture sequential correlations from higher-level sequence representations. Then the HFEM algorithm and its hierarchical feature extraction architecture are detailed. We establish the training, validation and test datasets, containing 72,532, 18,133, and 2679 mechanical patent documents, respectively, and then check the performance of HFEMs. Finally, we compared the results of the proposed HFEM and three other single neural network models, namely CNN, long–short-term memory (LSTM), and BiLSTM. The experimental results indicate that our proposed HFEM outperforms the other compared models in both precision and recall.