Instructions to use alphahg/kobigbird-bert-base_84756313 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alphahg/kobigbird-bert-base_84756313 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="alphahg/kobigbird-bert-base_84756313")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("alphahg/kobigbird-bert-base_84756313") model = AutoModelForQuestionAnswering.from_pretrained("alphahg/kobigbird-bert-base_84756313", device_map="auto") - Notebooks
- Google Colab
- Kaggle
kobigbird-bert-base_84756313
This model is a fine-tuned version of monologg/kobigbird-bert-base on the custom_squad_v2 dataset.
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: 32
- eval_batch_size: 32
- seed: 30
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 0.99 | 84 | 0.9980 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
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