colloquial-tamil-fineetuned

This model is a fine-tuned version of aishu15/colloquial-tamil-fineetuned on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0190

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
0.088	1.0	1997	0.0360
0.0592	2.0	3994	0.0236
0.0481	3.0	5991	0.0190

Framework versions

• Transformers 4.48.3
• Pytorch 2.5.1+cu124
• Datasets 3.3.2
• Tokenizers 0.21.0 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

Load model and tokenizer

model_name = "aishu15/colloquial-tamil-fineetuned" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

🔥 Example Usage

Load and test the model using Hugging Face Transformers:

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
# Load model and tokenizer
model_name = "aishu15/colloquial-tamil-fineetuned"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
# Function to translate text
def translate(text):
    inputs = tokenizer(text, return_tensors="pt")
    outputs = model.generate(**inputs, max_length=128)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)
# Example translations
test_sentences = [
    "This is so beautiful",
    "Bro, are you coming or not?",
    "My mom is gonna kill me if I don't reach home now!"
]
for sentence in test_sentences:
    print(f"English: {sentence}")
    print(f"Colloquial Tamil: {translate(sentence)}\n")