Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
Instructions to use raygx/BERT-NepSA-T2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use raygx/BERT-NepSA-T2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="raygx/BERT-NepSA-T2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("raygx/BERT-NepSA-T2") model = AutoModelForSequenceClassification.from_pretrained("raygx/BERT-NepSA-T2") - Notebooks
- Google Colab
- Kaggle
BERT-NepSA-T2
This model is a fine-tuned version of Shushant/nepaliBERT 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': 'AdamWeightDecay', 'learning_rate': 1e-06, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.0001}
- training_precision: float32
Training results
Framework versions
- Transformers 4.31.0
- TensorFlow 2.12.0
- Datasets 2.13.1
- Tokenizers 0.13.3
- Downloads last month
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Model tree for raygx/BERT-NepSA-T2
Base model
Shushant/nepaliBERT