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QA_SYNTH_03_OCT_WITH_FINETUNE_1.1
am-infoweb/QA_SYNTH_03_OCT_WITH_FINETUNE_1.1
QA_SYNTH_03_OCT_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
- Oct 3, 2023
- Updated
- Oct 3, 2023
- SHA
- 0f4966da
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_03_OCT_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: 0.0000
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 |
|---|---|---|---|
| 0.4121 | 1.0 | 25566 | 0.3383 |
| 0.3399 | 2.0 | 51132 | 0.3236 |
| 0.3087 | 3.0 | 76698 | 0.3220 |
| 0.3544 | 4.0 | 102264 | 0.3204 |
| 0.3487 | 5.0 | 127830 | 0.3201 |
| 0.0066 | 6.0 | 153396 | 0.0015 |
| 0.0034 | 7.0 | 178962 | 0.0001 |
| 0.0 | 8.0 | 204528 | 0.0000 |
| 0.0 | 9.0 | 230094 | 0.0000 |
| 0.0 | 10.0 | 255660 | 0.0000 |
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
- Oct 3, 2023
- Updated
- Oct 3, 2023