transformers tensorboard safetensors m2m_100 text2text-generation generated_from_trainer text-to-text ta en dataset:aishu15/english-tamil-colloquial dataset:aishu15/cooloquial-tam base_model:aishu15/colloquial-tamil-fineetuned base_model:finetune:aishu15/colloquial-tamil-fineetuned license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
base_model: aishu15/colloquial-tamil-fineetuned
datasets:
- aishu15/english-tamil-colloquial
- aishu15/cooloquial-tam
language:
- ta
- en
library_name: transformers
license: apache-2.0
tags:
- generated_from_trainer
- text-to-text
model-index:
- name: colloquial-tamil-fineetuned
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# colloquial-tamil-fineetuned

This model is a fine-tuned version of [aishu15/colloquial-tamil-fineetuned](https://huggingface.co/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**:  

```python
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")