Instructions to use badokorach/mobilebert-uncased-30-10-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use badokorach/mobilebert-uncased-30-10-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="badokorach/mobilebert-uncased-30-10-2")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("badokorach/mobilebert-uncased-30-10-2") model = AutoModelForQuestionAnswering.from_pretrained("badokorach/mobilebert-uncased-30-10-2") - Notebooks
- Google Colab
- Kaggle
mobilebert-uncased-30-10-2
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: 3.1286
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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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 |
|---|---|---|---|
| No log | 1.0 | 131 | 3.6617 |
| No log | 2.0 | 262 | 3.3484 |
| No log | 3.0 | 393 | 3.2335 |
| 100918.664 | 4.0 | 524 | 3.1511 |
| 100918.664 | 5.0 | 655 | 3.1286 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
- Downloads last month
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Model tree for badokorach/mobilebert-uncased-30-10-2
Base model
google/mobilebert-uncased