Welcome to the world of NLP innovation! Today we’re diving into the remarkable Sentence-Doctor, a T5 model designed to correct errors found in sentences across English, German, and French. This guide will walk you through how to use it effectively, troubleshoot common issues, and fine-tune the model for your own needs.

Understanding the Problem

In the realm of Natural Language Processing (NLP), many tasks rely on services such as text extraction libraries, Optical Character Recognition (OCR), Speech-to-Text libraries, and even Sentence Boundary Detection.

• These tasks often introduce errors that can derail the quality of any subsequent models, particularly since they are usually trained on clean input.

Introducing the Solution

The Sentence-Doctor model aims to combat these issues by reconstructing sentences based on their contextual surroundings. Think of it as a skilled linguist who is not just translating the words but piecing together the intended meaning from a jumbled narrative.

The primary capability of this model can be summarized as:

• Input an erroneous sentence and its context;
• The model reconstructs the intended sentence.

Use Cases

The potential applications for Sentence-Doctor are numerous:

• Repairing sentences obtained from noisy OCR outputs.
• Fixing sentence boundaries that may have been degraded.
• Performing sentence-level spelling corrections, albeit not the model’s main focus.

For instance, consider this German example:

Input: "und ich bin im"
Prefix_Context: "Hallo! Mein Name ist John",
Postfix_Context: "Januar 1990 geboren."
Output: "John und ich bin im Jahr 1990 geboren."

Or, in English:

Input: "I went to church las yesteday"
Output: "I went to church last Sunday."

Preprocessing the Input

Here’s how to prepare your input for the Sentence-Doctor model. Assume we start with the following text:

text = "That is my job I am a medical doctor I save lives"

However, the output after extraction might look like this:

sentences = ["That is my job I a", "m a medical doct", "I save lives"]

To correct the sentence “m a medical doct”, you can structure the input as follows:

input_text = "repair_sentence: " + sentences[1] + " context: {" + sentences[0] + "}{" + sentences[2] + "} "
print(input_text) # Will output "repair_sentence: m a medical doct context: {That is my job I a}{I save lives} "

The context is optional, and so the input could be simplified to:

repair_sentence: m a medical doct context: {}{} 

Making Inferences

Once your input is ready, here’s how to run inference using the Sentence-Doctor:

from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("flexudy/t5-base-multi-sentence-doctor")
model = AutoModelWithLMHead.from_pretrained("flexudy/t5-base-multi-sentence-doctor")
input_text = "repair_sentence: m a medical doct context: {That is my job I a}{I save lives} "
input_ids = tokenizer.encode(input_text, return_tensors="pt")
outputs = model.generate(input_ids, max_length=32, num_beams=1)
sentence = tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
assert sentence == "I am a medical doctor."

Fine-Tuning the Model

If you wish to improve Sentence-Doctor, a fine-tuning script is available:

# TODO Set your training epochs
config.TRAIN_EPOCHS = 3
# TODO Where is your data? Enter the path
trainer.start("data/sentence_doctor_dataset_300.csv")

Troubleshooting

As with any technology, issues may arise. Here are common troubleshooting tips:

• If the model isn’t performing well, consider fine-tuning it with your domain-specific dataset.
• Check your input formatting to ensure it adheres to the required structure.
• Make sure all dependencies, such as the Hugging Face transformers library, are correctly installed.

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.