Instructions to use vaibhav9/distilbert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vaibhav9/distilbert-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="vaibhav9/distilbert-base-uncased")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("vaibhav9/distilbert-base-uncased") model = AutoModelForQuestionAnswering.from_pretrained("vaibhav9/distilbert-base-uncased") - Notebooks
- Google Colab
- Kaggle
distilbert-base-uncased
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 3.8601
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.2968 | 1.0 | 3765 | 3.8488 |
| 3.1549 | 2.0 | 7530 | 3.8771 |
| 3.1105 | 3.0 | 11295 | 3.8601 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
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