Deberta V3 Large Boolq

🚀 Deberta-v3-large_boolq

This model is a fine - tuned version of microsoft/deberta-v3-large on the boolq dataset. It can be used for text classification tasks, achieving high accuracy on the boolq dataset.

🚀 Quick Start

This model is a fine - tuned version of microsoft/deberta-v3-large on the boolq dataset. It achieves the following results on the evaluation set:

• Loss: 0.4601
• Accuracy: 0.8835

✨ Features

• Fine - tuned on the boolq dataset, suitable for text classification tasks.
• Achieves high accuracy on the evaluation set.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("nfliu/deberta-v3-large_boolq")
tokenizer = AutoTokenizer.from_pretrained("nfliu/deberta-v3-large_boolq")

# Each example is a (question, context) pair.
examples = [
    ("Lake Tahoe is in California", "Lake Tahoe is a popular tourist spot in California."),
    ("Water is wet", "Contrary to popular belief, water is not wet.")
]

encoded_input = tokenizer(examples, padding=True, truncation=True, return_tensors="pt")

with torch.no_grad():
    model_output = model(**encoded_input)
    probabilities = torch.softmax(model_output.logits, dim=-1).cpu().tolist()

probability_no = [round(prob[0], 2) for prob in probabilities]
probability_yes = [round(prob[1], 2) for prob in probabilities]

for example, p_no, p_yes in zip(examples, probability_no, probability_yes):
    print(f"Question: {example[0]}")
    print(f"Context: {example[1]}")
    print(f"p(No | question, context): {p_no}")
    print(f"p(Yes | question, context): {p_yes}")
    print()

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 1e - 05
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e - 08
• lr_scheduler_type: linear
• num_epochs: 5.0

Training results

Framework versions

• Transformers 4.32.1
• Pytorch 2.0.1+cu117
• Datasets 2.14.4
• Tokenizers 0.13.3

📄 License

This model is licensed under the MIT license.

📋 Model Information