# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("itsamitkumar/qamodel2")
model = AutoModelForQuestionAnswering.from_pretrained("itsamitkumar/qamodel2")Quick Links
qamodel2
This model is a fine-tuned version of deepset/roberta-base-squad2 on the subjqa dataset. It achieves the following results on the evaluation set:
- Loss: 1.7777
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: 1e-06
- 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: 8
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.9544 | 1.0 | 81 | 2.0493 |
| 1.7079 | 2.0 | 162 | 1.8907 |
| 1.6135 | 3.0 | 243 | 1.8236 |
| 1.5829 | 4.0 | 324 | 1.7984 |
| 1.5381 | 5.0 | 405 | 1.7900 |
| 1.515 | 6.0 | 486 | 1.7827 |
| 1.5079 | 7.0 | 567 | 1.7827 |
| 1.4952 | 8.0 | 648 | 1.7777 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- 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="itsamitkumar/qamodel2")