# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("phdev/dynamic_tinybert-finetuned-squad")
model = AutoModelForQuestionAnswering.from_pretrained("phdev/dynamic_tinybert-finetuned-squad")Quick Links
dynamic_tinybert-finetuned-squad
This model is a fine-tuned version of Intel/dynamic_tinybert on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.4682
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 |
|---|---|---|---|
| 0.5086 | 1.0 | 5533 | 1.1495 |
| 0.3869 | 2.0 | 11066 | 1.2774 |
| 0.2659 | 3.0 | 16599 | 1.4682 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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Model tree for phdev/dynamic_tinybert-finetuned-squad
Base model
Intel/dynamic_tinybert
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="phdev/dynamic_tinybert-finetuned-squad")