Question Answering
Transformers
PyTorch
TensorBoard
gpt2
Generated from Trainer
text-generation-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("vaibhav9/GPT2-qa")
model = AutoModelForQuestionAnswering.from_pretrained("vaibhav9/GPT2-qa", device_map="auto")Quick Links
GPT2-qa
This model is a fine-tuned version of gpt2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 4.2957
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: 2
- eval_batch_size: 2
- 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 |
|---|---|---|---|
| 4.1563 | 1.0 | 30926 | 4.6165 |
| 3.7545 | 2.0 | 61852 | 3.9101 |
| 3.3745 | 3.0 | 92778 | 4.2957 |
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/GPT2-qa")