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squad_qna_model
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset. It achieves the following results on the evaluation set:
- Loss: 0.1156
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.2903 | 0.23 | 5000 | 0.1721 |
| 0.1883 | 0.46 | 10000 | 0.1771 |
| 0.143 | 0.68 | 15000 | 0.1278 |
| 0.1952 | 0.91 | 20000 | 0.1156 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3
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Model tree for Clyvey/squad_qna_model
Base model
distilbert/distilbert-base-uncased
# Load model directly from transformers import AutoTokenizer, AutoModelForMultipleChoice tokenizer = AutoTokenizer.from_pretrained("Clyvey/squad_qna_model") model = AutoModelForMultipleChoice.from_pretrained("Clyvey/squad_qna_model")