Instructions to use bhaveshadhikari/roberta-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bhaveshadhikari/roberta-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="bhaveshadhikari/roberta-finetuned")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("bhaveshadhikari/roberta-finetuned") model = AutoModelForMaskedLM.from_pretrained("bhaveshadhikari/roberta-finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
roberta-finetuned
This model is a fine-tuned version of IRIIS-RESEARCH/RoBERTa_Nepali_125M on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.8258
- Model Preparation Time: 0.0028
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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- 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
- lr_scheduler_warmup_steps: 500
- training_steps: 25000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time |
|---|---|---|---|---|
| 2.7798 | 0.02 | 500 | 2.5232 | 0.0028 |
| 2.4726 | 1.0066 | 1000 | 2.2927 | 0.0028 |
| 2.3473 | 1.0266 | 1500 | 2.1815 | 0.0028 |
| 2.2124 | 2.0133 | 2000 | 2.1117 | 0.0028 |
| 2.15 | 2.0333 | 2500 | 2.0413 | 0.0028 |
| 2.0669 | 3.0199 | 3000 | 2.0138 | 0.0028 |
| 2.0006 | 4.0066 | 3500 | 1.9784 | 0.0028 |
| 1.9425 | 4.0266 | 4000 | 1.9815 | 0.0028 |
| 1.9012 | 5.0132 | 4500 | 1.9378 | 0.0028 |
| 1.9179 | 5.0332 | 5000 | 1.9062 | 0.0028 |
| 1.8373 | 6.0198 | 5500 | 1.8962 | 0.0028 |
| 1.792 | 7.0065 | 6000 | 1.8855 | 0.0028 |
| 1.7343 | 7.0265 | 6500 | 1.8661 | 0.0028 |
| 1.8189 | 8.0131 | 7000 | 1.8380 | 0.0028 |
| 1.7639 | 8.0331 | 7500 | 1.8170 | 0.0028 |
| 1.7031 | 9.0198 | 8000 | 1.8233 | 0.0028 |
| 1.6691 | 10.0064 | 8500 | 1.8298 | 0.0028 |
| 1.6316 | 10.0264 | 9000 | 1.8258 | 0.0028 |
Framework versions
- Transformers 4.53.3
- Pytorch 2.6.0+cu124
- Datasets 4.4.1
- Tokenizers 0.21.2
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Model tree for bhaveshadhikari/roberta-finetuned
Base model
IRIIS-RESEARCH/RoBERTa_Nepali_125M