Instructions to use joel4899/distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use joel4899/distilbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="joel4899/distilbert")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("joel4899/distilbert") model = AutoModelForQuestionAnswering.from_pretrained("joel4899/distilbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
distilbert
This model is a fine-tuned version of distilbert-base-cased-distilled-squad on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7833
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: 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
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.4964 | 1.0 | 332 | 1.2982 |
| 1.1319 | 2.0 | 664 | 1.0313 |
| 0.9839 | 3.0 | 996 | 0.7833 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0+cpu
- Datasets 3.0.1
- Tokenizers 0.20.0
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