Instructions to use sharifMunna/IndicBart_paraphrase with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sharifMunna/IndicBart_paraphrase with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sharifMunna/IndicBart_paraphrase") model = AutoModelForSeq2SeqLM.from_pretrained("sharifMunna/IndicBart_paraphrase", device_map="auto") - Notebooks
- Google Colab
- Kaggle
IndicBart_paraphrase
This model is a fine-tuned version of ai4bharat/IndicBART on the None dataset.
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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
Training results
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
- Downloads last month
- 13
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Model tree for sharifMunna/IndicBart_paraphrase
Base model
ai4bharat/IndicBART