03 Mar Text to Text Generations (Translate) using Hugging Face
In this lesson, we will use the Hugging Face Transformers library for text-to-text (text2text) translation. Models like T5, BART, and MarianMT are well-suited for translation tasks. Below is a step-by-step guide to performing translation using the Hugging Face Transformers library.
Text-to-text models typically require a task prefix to specify the type of task (e.g., translation, summarization, etc.). For example, Text2Text generation includes:
- Translation: “translate English to Spanish: …”
- Summarization: “summarize: …”
- Question Answering: “question: … answer: …”
- Paraphrasing
- Sentiment Classification
Text2Text vs Text Generation
Let us also see the difference between text2text and text generation:
The TextGeneration class is typically used for autoregressive text generation, where the model generates text sequentially, one token at a time. This is commonly used for tasks like:
- Story generation
- Dialogue systems
- Open-ended text completion
The Text2TextGeneration class is used for sequence-to-sequence (seq2seq) tasks, where the model takes an input sequence and generates an output sequence. This is commonly used for tasks like:
- Translation
- Summarization
- Paraphrasing
- Question answering
Note: We will run the codes on Google Colab
Text2Text Translation – Coding Example
Here’s a step-by-step guide on how to use the Hugging Face Transformers library for text-to-text tasks:
Install the Required Libraries
Ensure you have the transformers and torch (or tensorflow) libraries installed. On Google Colab, use the following command to install:
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!pip install transformers torch |
Load a Pre-trained Translation Model
Hugging Face provides pre-trained models for translation tasks. For example:
- T5: A versatile text-to-text model that can handle translation by prefixing the input with a task-specific prompt (e.g., “translate English to French:”).
- MarianMT: A model specifically fine-tuned for translation tasks.
Here’s how to load a T5 model for translation:
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from transformers import T5ForConditionalGeneration, T5Tokenizer # Load the pre-trained T5 model and tokenizer model_name = "t5-small" # You can use "t5-base", "t5-large", etc. tokenizer = T5Tokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name) |
Prepare the Input Text
For translation tasks, you need to prefix the input text with a task-specific prompt. For example:
- English to Spanish: “translate English to Spanish: …”
- English to German: “translate English to German: …”
Here’s an example for translating English to Spanish:
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input_text = "Translate English to Spanish: My name is Amit Diwan, and I love cricket." |
Tokenize the Input Text
Tokenize the input text into input IDs that the model can process:
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids |
Generate the Translated Text
Use the model to generate the translated text. You can customize the generation process with parameters like max_length, num_beams, etc.
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# Generate translation outputs = model.generate(input_ids, max_length=50) # Decode the output tokens to text translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) print("Translated Text:", translated_text) |
Example Output
For the input sentence:
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Translate English to Spanish: My name is Amit Diwan, and I love cricket. |
The output might look like after translating English to Spanish:
The above screenshot shows the following output:
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Translated Text: Ich spreche von Amit Diwan und ich liebe den Cricket. |
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