The Longformer-Base-4096 model is a powerful tool for handling long documents, especially in the context of question answering (QA) tasks. This blog will guide you through how to utilize this fine-tuned model effectively, explaining key concepts along the way.
What is Longformer?
Imagine trying to read a long novel while taking notes. Regular models, like the classic ones before Longformer, often struggle to keep track of everything on such a long journey (or text). Longformer is like a wise assistant who knows how to focus on certain parts—like your questions—while keeping an eye on the entire story. This model uses a unique method called sliding-window local attention, allowing it to manage up to 4096 tokens in a single read.
Getting Started with Model Training
This Longformer model was trained on the SQuAD v1 dataset using a Google Colab V100 GPU. If you want to try out the fine-tuning process yourself, you can find the Colab notebook here.
Key Considerations for Training
- Longformer uses sliding-window local attention by default.
- For question answering, ensure that all question tokens possess global attention.
- The LongformerForQuestionAnswering model automatically adjusts for global attention.
- Input sequences must include three separator tokens encoded as
s [questions] s [contexts]. - Always provide a batch of examples in
input_ids.
Model Performance Results
Here are the impressive metrics for this model:
| Metric | Value |
|---|---|
| Exact Match | 85.1466 |
| F1 Score | 91.5415 |
Model in Action
To use the Longformer model for question answering, follow this code snippet:
import torch
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("valhalla/longformer-base-4096-finetuned-squadv1")
model = AutoModelForQuestionAnswering.from_pretrained("valhalla/longformer-base-4096-finetuned-squadv1")
text = "Huggingface has democratized NLP. Huge thanks to Huggingface for this."
question = "What has Huggingface done?"
encoding = tokenizer(question, text, return_tensors="pt")
input_ids = encoding["input_ids"]
attention_mask = encoding["attention_mask"]
start_scores, end_scores = model(input_ids, attention_mask=attention_mask)
all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0].tolist())
answer_tokens = all_tokens[torch.argmax(start_scores) : torch.argmax(end_scores) + 1]
answer = tokenizer.decode(tokenizer.convert_tokens_to_ids(answer_tokens))
# output = democratized NLPUnderstanding the Code
Consider your task as a treasure hunt where each word and sentence of the text is a clue leading you to the answer. The code above does just that—
- Tokenizer Setup: The tokenizer serves as your map, turning questions and texts into input that the model understands.
- Model Initialization: The model is your detective, analyzing the clues (input) to find the correct answer.
- Encoding & Attention: These steps curate the information, ensuring that only the essential clues for the question are highlighted.
- Final Extraction: After piecing everything together, the model reveals the answer like a successful hunt!
Troubleshooting
If you encounter issues when running the model, here are some troubleshooting tips:
- Ensure that your environment has necessary libraries like
transformersandtorchinstalled. - Verify the model and tokenizer names; even a small typo can lead to errors.
- If your input text is too long, trim it down to fit within the 4096 token limit.
- For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
Conclusion
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.
