Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use Abhi964/MahaPhrase_IndicBERT_Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Abhi964/MahaPhrase_IndicBERT_Large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Abhi964/MahaPhrase_IndicBERT_Large")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Abhi964/MahaPhrase_IndicBERT_Large") model = AutoModelForSequenceClassification.from_pretrained("Abhi964/MahaPhrase_IndicBERT_Large") - Notebooks
- Google Colab
- Kaggle
MahaPhrase_IndicBERT_Large
This model is a fine-tuned version of ai4bharat/IndicBERTv2-MLM-Back-TLM on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1604
- Accuracy: 0.96
- F1: 0.9597
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 282 | 0.1638 | 0.944 | 0.9438 |
| 0.2466 | 2.0 | 564 | 0.2162 | 0.952 | 0.9517 |
| 0.2466 | 3.0 | 846 | 0.1604 | 0.96 | 0.9597 |
Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
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Model tree for Abhi964/MahaPhrase_IndicBERT_Large
Base model
ai4bharat/IndicBERTv2-MLM-Back-TLM