# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("intanm/mbert-squad2-webis")
model = AutoModelForQuestionAnswering.from_pretrained("intanm/mbert-squad2-webis")Quick Links
T-BASELINE001
20230430-001-baseline-mbert-qa-squadv2-ft-clickbait-spoiling
This model is a fine-tuned version of intanm/mbert-squadv2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 4.8747
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 |
|---|---|---|---|
| No log | 1.0 | 200 | 2.8065 |
| No log | 2.0 | 400 | 2.8088 |
| 2.5698 | 3.0 | 600 | 3.1652 |
| 2.5698 | 4.0 | 800 | 3.5464 |
| 1.1384 | 5.0 | 1000 | 3.8477 |
| 1.1384 | 6.0 | 1200 | 4.1725 |
| 1.1384 | 7.0 | 1400 | 4.5057 |
| 0.4763 | 8.0 | 1600 | 4.7721 |
| 0.4763 | 9.0 | 1800 | 4.8970 |
| 0.2594 | 10.0 | 2000 | 4.8747 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="intanm/mbert-squad2-webis")