Instructions to use hariaravind/hindi_tamil_bpcc_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hariaravind/hindi_tamil_bpcc_model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("hariaravind/hindi_tamil_bpcc_model") model = AutoModelForSeq2SeqLM.from_pretrained("hariaravind/hindi_tamil_bpcc_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
hindi_tamil_bpcc_model
This model is a fine-tuned version of google-t5/t5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3718
- Bleu: 0.0
- Gen Len: 19.0
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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|---|---|---|---|---|---|
| 0.4218 | 1.0 | 500 | 0.3773 | 0.0 | 19.016 |
| 0.3937 | 2.0 | 1000 | 0.3729 | 0.0 | 19.0 |
| 0.3899 | 3.0 | 1500 | 0.3718 | 0.0 | 19.0 |
Framework versions
- Transformers 5.13.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
- Downloads last month
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Model tree for hariaravind/hindi_tamil_bpcc_model
Base model
google-t5/t5-small