longformer base 4096

Introduction
Longformer is a transformer model designed for handling long documents. It builds upon a RoBERTa checkpoint and is pretrained for masked language modeling (MLM) on documents with sequences up to 4,096 tokens. The model employs a combination of sliding window (local) attention and global attention, which can be configured based on the task requirements.

Architecture
Longformer utilizes a hybrid attention mechanism:

• Sliding Window (Local) Attention: This allows the model to focus on a series of tokens within a specific range.
• Global Attention: Configurable by the user, enabling the model to learn task-specific representations by focusing on important tokens across the entire sequence.

Training
The longformer-base-4096 model is trained from the RoBERTa checkpoint with an emphasis on MLM for long document handling. The training process involves configuring global attention to suit specific tasks, with examples provided in the model's documentation and the referenced paper.

Guide: Running Locally

1. Setup Environment: Ensure you have Python and PyTorch installed.
2. Install Transformers Library: Use pip to install the Hugging Face Transformers library.

   pip install transformers
   

3. Download Model: Utilize the Transformers library to load the Longformer model.

   from transformers import LongformerModel, LongformerTokenizer
   model = LongformerModel.from_pretrained("allenai/longformer-base-4096")
   tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer-base-4096")
   

4. Run Inference: Tokenize inputs and run them through the model to obtain outputs.

   inputs = tokenizer("Your input text here", return_tensors="pt")
   outputs = model(**inputs)
   

For enhanced performance, consider using cloud GPUs such as those offered by AWS, GCP, or Azure, which are well-suited for training and deploying models like Longformer.

License
The Longformer model is released under the Apache 2.0 License, allowing for open-source use and modification within the terms specified in the license.