# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("ShynBui/s17")
model = AutoModelForQuestionAnswering.from_pretrained("ShynBui/s17")Quick Links
s17
This model is a fine-tuned version of bert-base-cased on the 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
- Downloads last month
- 1
Model tree for ShynBui/s17
Base model
google-bert/bert-base-cased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="ShynBui/s17")