# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("intanm/mbert-squadv2")
model = AutoModelForQuestionAnswering.from_pretrained("intanm/mbert-squadv2")Quick Links
mbert-squadv2
This model is a fine-tuned version of bert-base-multilingual-cased on the squad_v2 dataset. It achieves the following results on the evaluation set:
- Loss: 0.9959
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 |
|---|---|---|---|
| 0.9459 | 1.0 | 8145 | 0.9588 |
| 0.6985 | 2.0 | 16290 | 0.9959 |
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-squadv2")