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--- |
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language: |
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- fa |
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metrics: |
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- f1 |
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- accuracy |
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- precision |
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- recall |
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base_model: |
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- sbunlp/fabert |
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pipeline_tag: text-classification |
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tags: |
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- code |
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--- |
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# **Fine-Tuned FaBERT Model for Formality Classification** |
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This repository contains a fine-tuned version of **FABERT**, a pre-trained language model designed for **formality classification**. This model has been specifically trained to classify text as **formal** or **informal**, making it ideal for applications in content moderation, social media monitoring, and customer support automation. |
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## **Model Overview** |
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- **Architecture:** Built on the **FABERT** model, a transformer-based architecture optimized for NLP tasks. |
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- **Task:** **Formality Classification** – distinguishing between formal and informal language in text. |
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- **Fine-Tuning:** The model has been fine-tuned on a custom dataset containing a variety of formal and informal text. |
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## **Key Features** |
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- **Multilingual Support:** This model is capable of classifying text in multiple languages, ensuring robustness in diverse linguistic contexts. |
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- **High Performance:** Fine-tuned to provide accurate predictions for formal vs. informal text classification. |
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- **Efficient for Deployment:** Optimized for real-time use in environments like social media platforms, content moderation tools, and communication systems. |
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## **How to Use the Model** |
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You can use this model in your Python code with the Hugging Face `transformers` library and PyTorch. The following code snippet demonstrates how to tokenize text, make predictions, and classify whether the text is formal or informal. |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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# Load the pre-trained tokenizer and model |
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tokenizer = AutoTokenizer.from_pretrained("faimlab/fabert_formality_classifier") |
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model = AutoModelForSequenceClassification.from_pretrained("faimlab/fabert_formality_classifier") |
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# Ensure the model runs on GPU if available |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model.to(device) |
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# Example input text |
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input_text = "Please find attached the report for your review." |
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# Tokenize the input |
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inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True, max_length=512) |
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# Move the model and input to GPU if available |
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inputs = {key: value.to(device) for key, value in inputs.items()} |
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# Make predictions |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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logits = outputs.logits |
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# Get the predicted label |
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predicted_label = logits.argmax(dim=1).item() |
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print(f"Predicted Label: {predicted_label}") |
