Instructions to use kallacharanteja/mt5-small-to-Hin-hard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use kallacharanteja/mt5-small-to-Hin-hard with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("google/mt5-small") model = PeftModel.from_pretrained(base_model, "kallacharanteja/mt5-small-to-Hin-hard") - Transformers
How to use kallacharanteja/mt5-small-to-Hin-hard with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("kallacharanteja/mt5-small-to-Hin-hard", dtype="auto") - Notebooks
- Google Colab
- Kaggle
mt5-small-to-Hin-hard
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: 3.3676
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAFACTOR and the args are: No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.03
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 18.0774 | 1.9332 | 500 | 3.3676 |
Framework versions
- PEFT 0.18.1
- Transformers 5.2.0
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.2
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Base model
google/mt5-small