Instructions to use EricPeter/mobilebert-uncased-squad-v2-30-10-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EricPeter/mobilebert-uncased-squad-v2-30-10-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="EricPeter/mobilebert-uncased-squad-v2-30-10-2")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("EricPeter/mobilebert-uncased-squad-v2-30-10-2") model = AutoModelForQuestionAnswering.from_pretrained("EricPeter/mobilebert-uncased-squad-v2-30-10-2") - Notebooks
- Google Colab
- Kaggle
mobilebert-uncased-squad-v2-30-10-2
This model is a fine-tuned version of csarron/mobilebert-uncased-squad-v2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0000
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: 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.628 | 1.0 | 9048 | 0.9281 |
| 0.2131 | 2.0 | 18096 | 0.1117 |
| 0.0784 | 3.0 | 27144 | 0.0231 |
| 0.0287 | 4.0 | 36192 | 0.0005 |
| 0.0096 | 5.0 | 45240 | 0.0000 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.14.7
- Tokenizers 0.15.0
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Model tree for EricPeter/mobilebert-uncased-squad-v2-30-10-2
Base model
csarron/mobilebert-uncased-squad-v2