Instructions to use pbwinter/hindi-masked-t5-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pbwinter/hindi-masked-t5-small with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("pbwinter/hindi-masked-t5-small") model = AutoModelForSeq2SeqLM.from_pretrained("pbwinter/hindi-masked-t5-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
hindi-masked-t5-small
This model is a fine-tuned version of t5-small on an unknown dataset. It achieves the following results on the evaluation set:
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:
- optimizer: {'name': 'RMSprop', 'learning_rate': 0.001, 'decay': 0.0, 'rho': 0.9, 'momentum': 0.0, 'epsilon': 1e-07, 'centered': False}
- training_precision: float32
Training results
Framework versions
- Transformers 4.32.1
- TensorFlow 2.10.0
- Datasets 2.17.0
- Tokenizers 0.13.3
- Downloads last month
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Model tree for pbwinter/hindi-masked-t5-small
Base model
google-t5/t5-small