# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("aaaacash/fine-tuned-bert-mini-5")
model = AutoModelForQuestionAnswering.from_pretrained("aaaacash/fine-tuned-bert-mini-5", device_map="auto")Quick Links
fine-tuned-bert-mini-5
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7607
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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.4197 | 1.0 | 3765 | 0.7540 |
| 0.4152 | 2.0 | 7530 | 0.7607 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="aaaacash/fine-tuned-bert-mini-5")