Instructions to use sabrinah/mobileBERT-PoA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sabrinah/mobileBERT-PoA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="sabrinah/mobileBERT-PoA")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("sabrinah/mobileBERT-PoA") model = AutoModelForQuestionAnswering.from_pretrained("sabrinah/mobileBERT-PoA") - Notebooks
- Google Colab
- Kaggle
mobileBERT-PoA
This model is a fine-tuned version of google/mobilebert-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 4.8735
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: 5
- eval_batch_size: 5
- 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 |
|---|---|---|---|
| No log | 1.0 | 7 | 118892.4922 |
| No log | 2.0 | 14 | 5.6600 |
| No log | 3.0 | 21 | 4.8735 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cpu
- Datasets 2.14.5
- Tokenizers 0.13.3
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Model tree for sabrinah/mobileBERT-PoA
Base model
google/mobilebert-uncased