<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
bert-base-uncased-finetuned-tquad
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7603
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.8078 | 1.0 | 1050 | 0.7037 |
| 0.6704 | 2.0 | 2100 | 0.7388 |
| 0.5875 | 3.0 | 3150 | 0.7603 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
Pipeline
dataset = load_dataset("dilanbakr/tquad")
tokenizer = AutoTokenizer.from_pretrained("dilanbakr/bert-base-uncased-finetuned-tquad")
model = AutoModelForQuestionAnswering.from_pretrained("dilanbakr/bert-base-uncased-finetuned-tquad")
nlp=pipeline("question-answering", model=model, tokenizer=tokenizer)
print(nlp(question="Anaheim hangi ülkenin rock grubudur?", context="Anaheim, Kaliforniya'da kurulmuş olan Amerikan rock grubudur."))
