FN_JLL-Hackathon-Fine-Tuned_QA-ALBERT-SQuAD
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: 1.3848
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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 |
|---|---|---|---|
| No log | 1.0 | 25 | 2.4216 |
| No log | 2.0 | 50 | 1.5827 |
| No log | 3.0 | 75 | 1.3848 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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Model tree for FredNajjar/FN_JLL-Hackathon-Fine-Tuned_QA-ALBERT-SQuAD
Base model
albert/albert-base-v2
Finetuned
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