Instructions to use bturley/bert-squad-control-tiny-rep_20251123_173537 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bturley/bert-squad-control-tiny-rep_20251123_173537 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="bturley/bert-squad-control-tiny-rep_20251123_173537")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("bturley/bert-squad-control-tiny-rep_20251123_173537") model = AutoModelForQuestionAnswering.from_pretrained("bturley/bert-squad-control-tiny-rep_20251123_173537", device_map="auto") - Notebooks
- Google Colab
- Kaggle
bert-squad-control-tiny-rep_20251123_173537
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.5544
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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.974 | 1.0 | 255 | 1.7897 |
| 1.4518 | 2.0 | 510 | 1.5544 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.9.0+cu126
- Datasets 4.4.1
- Tokenizers 0.22.1
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Model tree for bturley/bert-squad-control-tiny-rep_20251123_173537
Base model
google-bert/bert-base-uncased