In this article, we’ll explore how to work with a fine-tuned version of dbmdzbert-base-turkish-cased for zero-shot tasks in Turkish. Through a simple usage example, we’ll show how to prepare your data, run the model, and interpret the results. Let’s dive into the world of Natural Language Processing (NLP)!

What Is This Model?

This model is a specialized version fine-tuned for Natural Language Inference (NLI) tasks and has been adapted for use in Turkish. You can think of it as a chef who has learned to prepare Turkish dishes in a kitchen where general recipes are usually made. It takes advantage of the power of BERT, which works exceptionally well for understanding language nuances.

Setting Up Your Environment

Before you begin, make sure you have the required libraries. You will need Python, TensorFlow, and Transformers. Install them using pip if you haven’t done so yet:

pip install tensorflow transformers

Basic Usage

Here’s a simple example to get you started:

import time
import tensorflow as tf
from transformers import TFAutoModel, AutoTokenizer

texts = ["Galatasaray, bu akşamki maçın ardından şampiyonluğunu ilan etmeye hazırlanıyor."]
labels = ["spor", "siyaset", "kültür"]

model_name = "mysbert-base-turkish-cased-nli-mean"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = TFAutoModel.from_pretrained(model_name)

def label_text(model, tokenizer, texts, labels):
    texts_length = len(texts)
    tokens = tokenizer(texts + labels, padding=True, return_tensors='tf')
    embs = model(**tokens)[0]
    attention_masks = tf.cast(tokens['attention_mask'], tf.float32)
    sample_length = tf.reduce_sum(attention_masks, axis=-1, keepdims=True)
    masked_embs = embs * tf.expand_dims(attention_masks, axis=-1)
    masked_embs = tf.reduce_sum(masked_embs, axis=1) / tf.cast(sample_length, tf.float32)
    dists = tf.experimental.numpy.inner(masked_embs[:texts_length], masked_embs[texts_length:])
    scores = tf.nn.softmax(dists)
    results = list(zip(labels, scores.numpy().squeeze().tolist()))
    sorted_results = sorted(results, key=lambda x: x[1], reverse=True)
    sorted_results = [{ 'label': label, 'score': f"{score:.4f}" } for label, score in sorted_results]
    return sorted_results

start = time.time()
sorted_results = label_text(model, tokenizer, texts, labels)
elapsed = time.time() - start
print(sorted_results)
print(f"Processed in {elapsed:.2f} secs")

Breaking Down the Code

Imagine the model as a detective, trying to solve a mystery (understanding which label fits the input text) using clues (the token embeddings). Here’s how the function operates:

• Data Gathering: The model collects the “clues” from both the input text and labels.
• Tokenization: Just like a detective categorizes evidence, we tokenize our input to prepare it for analysis.
• Embeddings Generation: The detective then transforms these tokens into embeddings (clue analyses), which the model can understand.
• Attention Masking: Some clues may be more important than others. The attention mask helps focus on key pieces of information.
• Calculating Similarities: Finally, the model measures how similar the clues are to the labels and makes a decision based on this analysis.

Interpreting the Results

After running the function, you will get a list of labels with corresponding scores indicating the strength of each label’s fit to your input text. The output is formatted neatly, showing which labels performed best.

Troubleshooting

Sometimes things might not go as planned. Here are a few troubleshooting steps:

• Make sure you have the latest versions of TensorFlow and Transformers installed.
• Check that your input texts and labels are correctly formatted and match expectations.
• If you encounter any errors, carefully read the error messages, as they often provide clues about what went wrong.
• Experiment with different input texts to see how the model responds. It’s a learning process!

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Conclusion

Using a fine-tuned BERT model for NLI tasks in Turkish is not only exciting but also a valuable skill in the ever-evolving field of natural language processing. As we continue to explore such innovations, remember that these capabilities will help build more intelligent AI systems.

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.