Downloads · 30 days
30
5% of all-time downloads
albert-large-v2-finetuned-squad
lauraparra28/albert-large-v2-finetuned-squad
albert-large-v2-finetuned-squad is a question answering model from lauraparra28. Use it when the input is a question plus a passage. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
All-time downloads
595
Public
Repo size
9.3 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin66.6 MB · 97%
At a glance
- Task
- Question Answering
- Library
- transformers
- License
- apache-2.0
- Model type
- albert
- Access
- Public
- Created
- Oct 12, 2023
- Updated
- Oct 14, 2023
- SHA
- 880643f8
Try a prompt
Base models
Datasets
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
albert-large-v2-finetuned-squad
This model is a fine-tuned version of albert-large-v2 on the squad_v2 dataset. It achieves the following results on the evaluation set:
- Loss: 1.1819
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.8412 | 1.0 | 8248 | 0.8427 |
| 0.6729 | 2.0 | 16496 | 0.7978 |
| 0.5069 | 3.0 | 24744 | 0.8760 |
| 0.357 | 4.0 | 32992 | 1.0229 |
| 0.2374 | 5.0 | 41240 | 1.1819 |
Framework versions
- Transformers 4.34.0
- Pytorch 1.12.1
- Datasets 2.14.5
- Tokenizers 0.14.1
- Task
- Question Answering
- Library
- transformers
- Type
- albert
- License
- apache-2.0
- Created
- Oct 12, 2023
- Updated
- Oct 14, 2023