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--- |
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library_name: peft |
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license: apache-2.0 |
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base_model: unsloth/mistral-7b-bnb-4bit |
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tags: |
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- unsloth |
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- generated_from_trainer |
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model-index: |
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- name: english-hindi-colloquial-translator |
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results: [] |
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datasets: |
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- vanshikasundrani/english-hindi-colloquial-dataset |
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- cfilt/iitb-english-hindi |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# english-hindi-colloquial-translator |
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This model is a fine-tuned version of [unsloth/mistral-7b-bnb-4bit](https://huggingface.co/unsloth/mistral-7b-bnb-4bit) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 8.7228 |
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## Model description |
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Model to translate the English to Hindi Language |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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Base pretrained dataset: |
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Training and Evaluation Data is present in: vanshikasundrani/english-hindi-colloquial-dataset |
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prompt = f"""### Human: You are a Hindi colloquial language translator. Translate the following English text to Hindi colloquial language (spoken Hindi). |
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Here are some examples: |
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"See you later?" -> ""बाद में मिलते हैं?" |
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"How are you?" -> "कैसे हो?" |
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"What's your plan?" -> "तुम्हारा क्या प्लान है?" |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0003 |
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- train_batch_size: 12 |
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- eval_batch_size: 12 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 2 |
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- num_epochs: 8 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 4.6198 | 5.9701 | 400 | 8.7228 | |
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### Framework versions |
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- PEFT 0.14.0 |
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- Transformers 4.48.3 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.3.2 |
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- Tokenizers 0.21.0 |
