Instructions to use kallidavidson/TinyBERT_General_4L_312D with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kallidavidson/TinyBERT_General_4L_312D with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="kallidavidson/TinyBERT_General_4L_312D")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("kallidavidson/TinyBERT_General_4L_312D") model = AutoModelForQuestionAnswering.from_pretrained("kallidavidson/TinyBERT_General_4L_312D") - Notebooks
- Google Colab
- Kaggle
TinyBERT_General_4L_312D
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 5.2060
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 |
|---|---|---|---|
| No log | 1.0 | 63 | 4.9456 |
| No log | 2.0 | 126 | 5.1836 |
| No log | 3.0 | 189 | 5.2060 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cpu
- Datasets 2.18.0
- Tokenizers 0.15.2
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