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indic-compose-mt5
This model is a fine-tuned version of google/mt5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: nan
- Bleu: 0.0
- Rouge1: 0.0
- Rougel: 0.0
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: 0.0005
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAFACTOR and the args are: No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Rouge1 | Rougel |
|---|---|---|---|---|---|---|
| 0.0 | 1.0 | 139 | nan | 0.0 | 0.0 | 0.0 |
| 0.0 | 2.0 | 278 | nan | 0.0 | 0.0 | 0.0 |
| 0.0 | 3.0 | 417 | nan | 0.0 | 0.0 | 0.0 |
Framework versions
- Transformers 4.57.6
- Pytorch 2.9.0+cu126
- Datasets 4.8.3
- Tokenizers 0.22.2
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Model tree for lalit127/indic-compose-mt5
Base model
google/mt5-small
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("lalit127/indic-compose-mt5") model = AutoModelForSeq2SeqLM.from_pretrained("lalit127/indic-compose-mt5")