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  ---
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- library_name: transformers
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- datasets:
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- - jarvisvasu/english-to-colloquial-tamil
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- language:
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- - en
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- - ta
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- base_model:
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- - suriya7/English-to-Tamil
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
 
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- # Model Card for Model ID
 
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- <!-- Provide a quick summary of what the model is/does. -->
 
 
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- ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- [More Information Needed]
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- **APA:**
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- [More Information Needed]
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- [More Information Needed]
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- ## More Information [optional]
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- ## Model Card Authors [optional]
 
 
 
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- ## Model Card Contact
 
 
 
 
 
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- [More Information Needed]
 
 
 
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  ---
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+ language:
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+ - en
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+ - ta
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+ license: cc-by-4.0
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+ tags:
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+ - translation
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+ - tamil
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+ - colloquial-tamil
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+ - fine-tuned
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+ - text-to-text
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+ datasets:
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+ - janisrebekahv/colloquial_tamil
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+ - jarvisvasu/english-to-colloquial-tamil
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+ - chatgpt-generated
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+ - youtube-comments
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+ model-index:
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+ - name: janisrebekahv/finetuned-colloquial-tamil
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+ results:
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+ - task:
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+ type: translation
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+ name: English to Colloquial Tamil
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+ dataset:
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+ name: janisrebekahv/colloquial_tamil
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+ type: text
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+ metrics:
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+ - name: BLEU Score
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+ type: bleu
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+ value: 38.5
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+ - name: ROUGE Score
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+ type: rouge
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+ value: 0.72
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  ---
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+ # janisrebekahv/finetuned-colloquial-tamil
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+ ## 📌 Model Overview
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+ This is a **fine-tuned version of [suriya7/English-to-Tamil](https://huggingface.co/suriya7/English-to-Tamil)**, trained to produce **colloquial Tamil translations** instead of formal Tamil.
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+ Translates **English Colloquial Tamil**
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+ ✅ Incorporates **slang, informal speech, and real-world phrasing**
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+ ✅ Useful for **chatbots, conversational AI, and social media applications**
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+ ---
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+ ## 📜 Dataset
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+ 🔹 **Custom Dataset Used for Fine-Tuning:**
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+ 📂 **[janisrebekahv/colloquial_tamil](https://huggingface.co/datasets/janisrebekahv/colloquial_tamil)**
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+ This dataset was specifically curated to train this model, improving its ability to translate **English to Colloquial Tamil** accurately.
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+ This model was fine-tuned on a **custom dataset**, which includes:
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+ 1️⃣ **[jarvisvasu/english-to-colloquial-tamil](https://huggingface.co/datasets/jarvisvasu/english-to-colloquial-tamil)** – A publicly available dataset for informal Tamil translations.
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+ 2️⃣ **YouTube Comments Dataset (Custom-Created)** – Extracted using the **YouTube API** and manually converted to colloquial Tamil for authenticity.
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+ 3️⃣ **ChatGPT-Generated Data** – Additional colloquial Tamil phrases aligned with natural speech patterns.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ 📝 **Total dataset size**: **16,269 sentence pairs**
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+ ---
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+ ## 🔥 Example Usage
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+ Load and test the model using **Hugging Face Transformers**:
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+ ```python
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+ from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
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+ # Load model and tokenizer
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+ model_name = "janisrebekahv/finetuned-colloquial-tamil"
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+ tokenizer = AutoTokenizer.from_pretrained(model_name)
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+ model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
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+ # Function to translate text
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+ def translate(text):
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+ inputs = tokenizer(text, return_tensors="pt")
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+ outputs = model.generate(**inputs, max_length=128)
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+ return tokenizer.decode(outputs[0], skip_special_tokens=True)
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+ # Example translations
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+ test_sentences = [
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+ "This is so beautiful",
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+ "Bro, are you coming or not?",
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+ "My mom is gonna kill me if I don't reach home now!"
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+ ]
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+ for sentence in test_sentences:
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+ print(f"English: {sentence}")
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+ print(f"Colloquial Tamil: {translate(sentence)}\n")