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QA_REDACTION_NOV1_19-a2
am-infoweb/QA_REDACTION_NOV1_19-a2
QA_REDACTION_NOV1_19-a2 is a question answering model from am-infoweb. 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. --
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At a glance
- Task
- Question Answering
- Library
- transformers
- License
- mit
- Model type
- xlm-roberta
- Access
- Public
- Created
- Nov 1, 2023
- Updated
- Nov 1, 2023
- SHA
- 13da41ee
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. -->
QA_REDACTION_NOV1_19-a2
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0226
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: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.0736 | 1.0 | 1350 | 0.0554 |
| 0.0382 | 2.0 | 2700 | 0.0306 |
| 0.0498 | 3.0 | 4050 | 0.0201 |
| 0.0591 | 4.0 | 5400 | 0.0211 |
| 0.0487 | 5.0 | 6750 | 0.0184 |
| 0.0284 | 6.0 | 8100 | 0.0141 |
| 0.0362 | 7.0 | 9450 | 0.0166 |
| 0.0384 | 8.0 | 10800 | 0.0190 |
| 0.0379 | 9.0 | 12150 | 0.0168 |
| 0.013 | 10.0 | 13500 | 0.0213 |
| 0.014 | 11.0 | 14850 | 0.0269 |
| 0.0166 | 12.0 | 16200 | 0.0226 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1
- Datasets 2.14.6
- Tokenizers 0.14.1
- Task
- Question Answering
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Nov 1, 2023
- Updated
- Nov 1, 2023