Instructions to use abhishekkumaribt/distillbert-finetuned-squadv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abhishekkumaribt/distillbert-finetuned-squadv2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="abhishekkumaribt/distillbert-finetuned-squadv2")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("abhishekkumaribt/distillbert-finetuned-squadv2") model = AutoModelForQuestionAnswering.from_pretrained("abhishekkumaribt/distillbert-finetuned-squadv2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
distillbert-finetuned-squadv2
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.3336
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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.4448 | 1.0 | 625 | 2.0059 |
| 1.4879 | 2.0 | 1250 | 2.0137 |
| 1.2069 | 3.0 | 1875 | 2.3336 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
- Downloads last month
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Model tree for abhishekkumaribt/distillbert-finetuned-squadv2
Base model
distilbert/distilbert-base-uncased