Modernbert Large Squad2 V0.1

🚀 ModernBERT-large-squad2-v0.1

このモデルは、rajpurkar/squad_v2データセットでanswerdotai/ModernBERT-largeをファインチューニングしたバージョンです。 トレーニング中に使用された最大シーケンス長は8192です。 モデルをロードするには、trust_remote_codeをTrueに設定する必要があります。

🚀 クイックスタート

以下のコードを使用して、このモデルを使った質問応答タスクを実行できます。

from transformers import pipeline

model_name = "praise2112/ModernBERT-large-squad2-v0.1"

# a) 予測を取得
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)

context = """Model Summary
ModernBERT is a modernized bidirectional encoder-only Transformer model (BERT-style) pre-trained on 2 trillion tokens of English and code data with a native context length of up to 8,192 tokens. ModernBERT leverages recent architectural improvements such as:

Rotary Positional Embeddings (RoPE) for long-context support.
Local-Global Alternating Attention for efficiency on long inputs.
Unpadding and Flash Attention for efficient inference.
ModernBERT’s native long context length makes it ideal for tasks that require processing long documents, such as retrieval, classification, and semantic search within large corpora. The model was trained on a large corpus of text and code, making it suitable for a wide range of downstream tasks, including code retrieval and hybrid (text + code) semantic search.

It is available in the following sizes:

ModernBERT-base - 22 layers, 149 million parameters
ModernBERT-large - 28 layers, 395 million parameters
For more information about ModernBERT, we recommend our release blog post for a high-level overview, and our arXiv pre-print for in-depth information.

ModernBERT is a collaboration between Answer.AI, LightOn, and friends."""

question = "Why was RoPE used in ModernBERT?"

res = nlp(question=question, context=context, max_seq_len=8192)

# {'score': 0.5530015826225281, 'start': 309, 'end': 334, 'answer': ' for long-context support'}

💻 使用例

基本的な使用法

from transformers import pipeline

model_name = "praise2112/ModernBERT-large-squad2-v0.1"

# a) 予測を取得
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)

context = """Model Summary
ModernBERT is a modernized bidirectional encoder-only Transformer model (BERT-style) pre-trained on 2 trillion tokens of English and code data with a native context length of up to 8,192 tokens. ModernBERT leverages recent architectural improvements such as:

Rotary Positional Embeddings (RoPE) for long-context support.
Local-Global Alternating Attention for efficiency on long inputs.
Unpadding and Flash Attention for efficient inference.
ModernBERT’s native long context length makes it ideal for tasks that require processing long documents, such as retrieval, classification, and semantic search within large corpora. The model was trained on a large corpus of text and code, making it suitable for a wide range of downstream tasks, including code retrieval and hybrid (text + code) semantic search.

It is available in the following sizes:

ModernBERT-base - 22 layers, 149 million parameters
ModernBERT-large - 28 layers, 395 million parameters
For more information about ModernBERT, we recommend our release blog post for a high-level overview, and our arXiv pre-print for in-depth information.

ModernBERT is a collaboration between Answer.AI, LightOn, and friends."""

question = "Why was RoPE used in ModernBERT?"

res = nlp(question=question, context=context, max_seq_len=8192)

# {'score': 0.5530015826225281, 'start': 309, 'end': 334, 'answer': ' for long-context support'}

📚 ドキュメント

トレーニング手順

トレーニングハイパーパラメータ

トレーニング中に使用されたハイパーパラメータは以下の通りです。

• learning_rate: 1e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 64
• optimizer: Use ExtendedOptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 4

トレーニング結果

フレームワークバージョン

• Transformers 4.48.0.dev0
• Pytorch 2.5.1+cu124
• Datasets 2.20.0
• Tokenizers 0.21.0

📄 ライセンス

このモデルはApache 2.0ライセンスの下で提供されています。