# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("Shushant/questionansweringmodel")
model = AutoModelForQuestionAnswering.from_pretrained("Shushant/questionansweringmodel", device_map="auto")Quick Links
questionansweringmodel
This model is a fine-tuned version of microsoft/deberta-v3-large on the squad dataset. It achieves the following results on the evaluation set:
- Loss: 2.9365
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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.7398 | 1.0 | 5520 | 0.7216 |
| 0.5263 | 2.0 | 11040 | 0.7513 |
| 0.3828 | 3.0 | 16560 | 0.8821 |
| 0.2411 | 4.0 | 22080 | 1.0024 |
| 0.1549 | 5.0 | 27600 | 1.3959 |
| 0.0996 | 6.0 | 33120 | 1.7559 |
| 0.0594 | 7.0 | 38640 | 2.0031 |
| 0.0393 | 8.0 | 44160 | 2.3654 |
| 0.0326 | 9.0 | 49680 | 2.7383 |
| 0.0162 | 10.0 | 55200 | 2.9365 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.11.0+cu113
- Datasets 2.9.0
- Tokenizers 0.13.2
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# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="Shushant/questionansweringmodel")