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QA_SYNTH_27_SEPT_WITH_FINETUNE_1.1
am-infoweb/QA_SYNTH_27_SEPT_WITH_FINETUNE_1.1
QA_SYNTH_27_SEPT_WITH_FINETUNE_1.1 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
- Sep 27, 2023
- Updated
- Sep 27, 2023
- SHA
- a4f5c7fe
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_SYNTH_27_SEPT_WITH_FINETUNE_1.1
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: 0.0034
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: 4
- eval_batch_size: 4
- 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 |
|---|---|---|---|
| 0.0675 | 1.0 | 7594 | 0.0691 |
| 0.0795 | 2.0 | 15188 | 0.0287 |
| 0.0163 | 3.0 | 22782 | 0.0592 |
| 0.0212 | 4.0 | 30376 | 0.0600 |
| 0.0013 | 5.0 | 37970 | 0.0222 |
| 0.0054 | 6.0 | 45564 | 0.0061 |
| 0.0028 | 7.0 | 53158 | 0.0038 |
| 0.0 | 8.0 | 60752 | 0.0026 |
| 0.0 | 9.0 | 68346 | 0.0045 |
| 0.0 | 10.0 | 75940 | 0.0034 |
Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
- Task
- Question Answering
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Sep 27, 2023
- Updated
- Sep 27, 2023