🌟 MGE-LLMs/SteelBERT 🌟

SteelBERT was pre-trained based on DeBERTa using a corpus of 4.2 million materials abstracts and 55,000 full-text steel articles, amounting to roughly 0.96 billion words. For self-supervised training, SteelBERT masked 15% of the tokens using Masked Language Modeling (MLM) — a universal and effective pretraining method for NLP tasks.

SteelBERT was trained to predict the representation of masked words by adjusting parameters across various network layers. We allocated 95% of the corpus for training and 5% for validation. The validation loss reached 1.158 after 840 hours of training.

Why DeBERTa? 🤔

We chose the DeBERTa structure due to its innovative approach. DeBERTa introduces a disentangled attention mechanism that handles long-range dependencies, crucial for comprehending complex material interactions.

The original DeBERTa model's extensive sub-word vocabulary could introduce noise during tokenization. To address this, we trained a specialized tokenizer, constructing a vocabulary specific to the steel domain. Despite the smaller training corpus, we maintained a consistent vocabulary scale of 128,100 words to capture latent knowledge.

Model Architecture 🏗️

SteelBERT comprises 188 million parameters and is constructed using 12 stacked Transformer encoders, each with 12 attention heads. We used the original DeBERTa code with similar configurations and size.

The maximum sentence length was set to 512 tokens, and training continued until the loss stopped decreasing. The pre-training procedure employed 8 NVIDIA A100 40GB GPUs for 840 hours, with a batch size of 576 sequences.

New Features 🆕

• Specialized Tokenizer 🛠️: Trained on a steel materials corpus to enhance accuracy, integrating insights from other material corpora.

• Consistent Vocabulary Scale 📏: Maintained at 128,100 words to capture precise latent knowledge.

• Efficient Training Configuration ⚙️: Utilized 8 NVIDIA A100 40GB GPUs for 840 hours with a batch size of 576 sequences.

• Enhanced Fine-tuning Capabilities 🎯: Facilitates efficient fine-tuning for specific downstream tasks, enhancing practical application versatility.

• Disentangled Attention Mechanism 🧠: Effectively manages long-range dependencies, inherited from DeBERTa.

Usage Example 🚀

from transformers import AutoTokenizer, AutoModel
import torch

# Check if GPU is available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

model_path = "MGE-LLMs/SteelBERT"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModel.from_pretrained(model_path).to(device)  # Move model to GPU if available

# Example list of texts
texts = [
    "A composite steel plate for marine construction was fabricated using 316L stainless steel.",
    "The use of composite materials in construction is growing rapidly.",
    "Advances in material science are leading to stronger and more durable steel products."
]

# Tokenize the texts
inputs = tokenizer(texts, return_tensors='pt', padding=True, truncation=True).to(device)

# Print tokenized texts
for i, input_ids in enumerate(inputs['input_ids']):
    text_tokens = tokenizer.convert_ids_to_tokens(input_ids)
    print(f"Tokens for text {i + 1}: {text_tokens}")

# Get [CLS] embeddings for each input text
with torch.no_grad():
    outputs = model(**inputs, output_hidden_states=True)

hidden_states = outputs.hidden_states
last_hidden_state = hidden_states[-1]
cls_embeddings = last_hidden_state[:, 0, :]

# Print the [CLS] token embeddings for each text
print("CLS embeddings for each text:")
print(cls_embeddings)