# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("mateiaass/albertbase-qa")
model = AutoModelForQuestionAnswering.from_pretrained("mateiaass/albertbase-qa")Quick Links
albertbase-qa
This model is a fine-tuned version of albert-base-v2 on the squad dataset. It achieves the following results on the evaluation set:
- Loss: 1.0115
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.897 | 1.0 | 4380 | 0.9106 |
| 0.671 | 2.0 | 8760 | 0.8892 |
| 0.476 | 3.0 | 13140 | 1.0115 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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Model tree for mateiaass/albertbase-qa
Base model
albert/albert-base-v2
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="mateiaass/albertbase-qa")