Downloads · 30 days
51
25% of all-time downloads
Finetuned_Question_Answering_Model
Safick-HF/Finetuned_Question_Answering_Model
Finetuned_Question_Answering_Model is a question answering model from Safick-HF. Use it when the input is a question plus a passage. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
All-time downloads
206
Public
Parameters
335M
10.7 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors1.3 GB · 100%
At a glance
- Task
- Question Answering
- Library
- transformers
- License
- mit
- Model type
- electra
- Access
- Public
- Created
- Mar 11, 2024
- Updated
- Apr 23, 2024
- SHA
- e2d68541
Try a prompt
Base models
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. -->
Finetuned_Question_Answering_Model
This model is a fine-tuned version of deepset/gelectra-large-germanquad on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0331
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: 5
- eval_batch_size: 10
- 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 |
|---|---|---|---|
| 0.1008 | 1.0 | 3 | 0.3691 |
| 0.177 | 2.0 | 6 | 0.2874 |
| 0.0763 | 3.0 | 9 | 0.2115 |
| 0.0065 | 4.0 | 12 | 0.1496 |
| 0.0075 | 5.0 | 15 | 0.0874 |
| 0.0231 | 6.0 | 18 | 0.0727 |
| 0.0059 | 7.0 | 21 | 0.0512 |
| 0.0302 | 8.0 | 24 | 0.0407 |
| 0.0017 | 9.0 | 27 | 0.0347 |
| 0.0015 | 10.0 | 30 | 0.0331 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2+cpu
- Datasets 2.18.0
- Tokenizers 0.15.2
- Task
- Question Answering
- Library
- transformers
- Type
- electra
- License
- mit
- Created
- Mar 11, 2024
- Updated
- Apr 23, 2024