Contradiction Psb Lds

🚀 contradiction-psb-lds

A model for identifying contradiction sentences in patents using PatentSBERTa. It maps sentences and paragraphs to a 768-dimensional dense vector space, useful for tasks like clustering or semantic search.

🚀 Quick Start

This is a sentence-transformers model. It simplifies the process of mapping sentences and paragraphs into a 768-dimensional dense vector space, which can be applied to tasks such as clustering or semantic search.

✨ Features

• Sentence Embedding: Maps sentences and paragraphs to a 768-dimensional dense vector space.
• Versatile Applications: Suitable for tasks like clustering and semantic search.

📦 Installation

To use this model, you need to install sentence-transformers:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

If you have sentence-transformers installed, you can use the model as follows:

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)

Advanced Usage

Without sentence-transformers, you can use the model in the following way. First, pass your input through the transformer model, and then apply the appropriate pooling operation on top of the contextualized word embeddings.

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)

📚 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 1128 with parameters:

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

Loss: sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss

Parameters of the fit()-Method:

{
    "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
}

Full Model Architecture

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})
)

Citing & Authors

The following pre-trained model was used: AI-Growth-Lab/PatentSBERTa