Instructions to use Amna100/Augmented-PreTraining-MLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Amna100/Augmented-PreTraining-MLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Amna100/Augmented-PreTraining-MLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Amna100/Augmented-PreTraining-MLM") model = AutoModelForMaskedLM.from_pretrained("Amna100/Augmented-PreTraining-MLM") - Notebooks
- Google Colab
- Kaggle
Augmented-PreTraining-MLM
This model is a fine-tuned version of microsoft/deberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 3.1688
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: 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: 3.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 5.1204 | 1.0 | 445 | 3.9255 |
| 3.685 | 2.0 | 890 | 3.3359 |
| 3.3332 | 3.0 | 1335 | 3.1859 |
Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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Model tree for Amna100/Augmented-PreTraining-MLM
Base model
microsoft/deberta-base