Bert Large Portuguese Cased Legal Mlm Nli Sts V1
Meet Bert Large Portuguese Cased Legal Mlm Nli Sts V1, a powerful AI model designed for sentence and paragraph mapping. It converts text into a 1024-dimensional dense vector space, perfect for tasks like clustering or semantic search. But what makes it unique? This model is specifically trained on legal sentences from around 30,000 documents, making it a valuable tool for the legal domain. Its architecture is based on the BERTimbau large model, fine-tuned for semantic textual similarity tasks. With its efficient design, it can handle a wide range of tasks, from text classification to information retrieval. Whether you're working in the legal field or just need a reliable model for text analysis, Bert Large Portuguese Cased Legal Mlm Nli Sts V1 is definitely worth exploring.
Table of Contents
Model Overview
The Current Model is a powerful tool for natural language processing tasks, specifically designed for the Portuguese language. It’s a sentence-transformers model, which means it can map sentences and paragraphs to a 1024-dimensional dense vector space. This allows for tasks like clustering or semantic search.
Capabilities
This model excels at:
- Semantic Search: It can map sentences and paragraphs to a dense vector space, making it perfect for tasks like clustering or semantic search.
- Textual Similarity: It’s been fine-tuned for Semantic Textual Similarity, allowing it to accurately determine the similarity between two pieces of text.
Primary Tasks
This model can perform various tasks, including:
- Clustering
- Text classification
- Document search
Strengths
The Current Model has several strengths that set it apart:
- Domain-specific training: It’s been trained on a large dataset of legal sentences, making it highly effective in the legal domain.
- High-dimensional vector space: It maps text to a 1024-dimensional dense vector space, allowing for accurate and efficient search and clustering.
Unique Features
This model offers several unique features:
- Multilingual capabilities: It’s been trained on a multilingual dataset, allowing it to understand and process text in multiple languages.
- Metadata Knowledge Distillation: It introduces a new technique for training large language models, making it more efficient and effective.
Performance
Current Model is a powerful tool for various natural language processing tasks. Let’s dive into its performance and explore its capabilities.
Speed
How fast can Current Model process text? With a maximum sequence length of 514, it can handle relatively long pieces of text. However, the actual processing speed depends on the specific task, the complexity of the input, and the computational resources available.
Accuracy
Current Model has been fine-tuned for various tasks, including semantic textual similarity (STS) and natural language inference (NLI). Its performance in these tasks is impressive, with a high degree of accuracy. For example, in the STS task, it can capture subtle differences in meaning between sentences.
Efficiency
Current Model is designed to be efficient in its use of computational resources. It uses a mean pooling mechanism to reduce the dimensionality of the input text, making it more manageable for downstream tasks. This approach also helps to reduce the risk of overfitting.
Limitations
Current Model is a powerful tool for semantic search, but it’s not perfect. Let’s take a closer look at some of its limitations.
Limited Domain Knowledge
Current Model was trained on a specific dataset of legal sentences from around 30,000 documents. While this makes it great for legal semantic search, it may not perform as well in other domains or industries.
Dependence on Training Data
The quality of Current Model’s performance is directly tied to the quality of the training data. If the training data contains biases or inaccuracies, these will be reflected in the model’s outputs.
Limited Contextual Understanding
While Current Model can understand the meaning of individual sentences, it may struggle to capture the broader context of a text.
Format
Current Model is a sentence-transformers model that maps sentences and paragraphs to a 1024-dimensional dense vector space. This allows it to be used for tasks like clustering or semantic search.
Architecture
The model is based on a transformer architecture, which is a type of neural network designed for sequence-to-sequence tasks. It uses a maximum sequence length of 514 tokens.
Data Formats
The model accepts input in the form of tokenized text sequences. This means that you need to pre-process your text data before feeding it into the model.
Input Requirements
To use the model, you need to provide a list of sentences or paragraphs that you want to embed into the vector space.
Example Use Cases
This model can be used in a variety of applications, such as:
- Document search: It can be used to search for relevant documents in a large database, based on their semantic meaning.
- Text classification: It can be used to classify text into different categories, based on its semantic meaning.
Here’s an example of how to use the model with the Hugging Face Transformers library:
from transformers import AutoTokenizer, AutoModel
import torch
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('stjiris/bert-large-portuguese-cased-legal-mlm-nli-sts-v1')
model = AutoModel.from_pretrained('stjiris/bert-large-portuguese-cased-legal-mlm-nli-sts-v1')
# Define a function for mean pooling
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)
# Tokenize input sentences
sentences = ['This is an example sentence', 'Each sentence is converted']
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform mean pooling
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
# Print sentence embeddings
print("Sentence embeddings:")
print(sentence_embeddings)
