# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("Ashmit06/roberta-base-squad2-finetuned-squad")
model = AutoModelForQuestionAnswering.from_pretrained("Ashmit06/roberta-base-squad2-finetuned-squad", device_map="auto")Quick Links
roberta-base-squad2-finetuned-squad
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0243
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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.0102 | 1.0 | 5351 | 0.0269 |
| 0.0045 | 2.0 | 10702 | 0.0243 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Ashmit06/roberta-base-squad2-finetuned-squad")