# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("smrynrz20/distilbert_qa_model")
model = AutoModelForQuestionAnswering.from_pretrained("smrynrz20/distilbert_qa_model")Quick Links
distilbert_qa_model
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.5930
- F1: 0.6367
- Exact Match: 0.517
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: 3.7185140364032e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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 | F1 | Exact Match |
|---|---|---|---|---|---|
| 1.2755 | 1.0 | 125 | 1.5176 | 0.6210 | 0.501 |
| 0.7661 | 2.0 | 250 | 1.5239 | 0.6361 | 0.515 |
| 0.6284 | 3.0 | 375 | 1.5930 | 0.6367 | 0.517 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for smrynrz20/distilbert_qa_model
Base model
distilbert/distilbert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="smrynrz20/distilbert_qa_model")