# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("vaibhav9/roberta-qa")
model = AutoModelForQuestionAnswering.from_pretrained("vaibhav9/roberta-qa")Quick Links
roberta-qa
This model is a fine-tuned version of deepset/roberta-base-squad2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7423
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 |
|---|---|---|---|
| 0.7278 | 1.0 | 3787 | 0.6452 |
| 0.4734 | 2.0 | 7574 | 0.6819 |
| 0.3543 | 3.0 | 11361 | 0.7423 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.11.0+cu102
- Datasets 2.8.0
- Tokenizers 0.13.2
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="vaibhav9/roberta-qa")