Instructions to use Pidus/bert-finetuned-squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pidus/bert-finetuned-squad with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Pidus/bert-finetuned-squad")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Pidus/bert-finetuned-squad") model = AutoModelForQuestionAnswering.from_pretrained("Pidus/bert-finetuned-squad") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
- bert_base_for_Q&A
- generated_from_trainer
model-index:
- name: bert-finetuned-squad
results: []
datasets:
- rajpurkar/squad
language:
- en
metrics:
- squad
pipeline_tag: question-answering
bert-finetuned-squad
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
Model description
Use of Bert-based-case
Training procedure
Use of Triner from huggingface from transformers library
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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: 3
- mixed_precision_training: Native AMP
Training results
- 'f1': 88.14385525453847
- 'exact_match': 80.7379375591296
Framework versions
- Transformers 5.0.0
- Pytorch 2.9.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2