# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("basilePlus/bert-finetuned-coqa")
model = AutoModelForQuestionAnswering.from_pretrained("basilePlus/bert-finetuned-coqa")Quick Links
bert-finetuned-coqa
This model is a fine-tuned version of basilePlus/bert-finetuned-squad on an unknown 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
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Model tree for basilePlus/bert-finetuned-coqa
Base model
basilePlus/bert-finetuned-squad
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="basilePlus/bert-finetuned-coqa")