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QA_SYNTH_DATA_WITH_UNANSWERABLE_23_AUG_xlm_roberta-base
am-infoweb/QA_SYNTH_DATA_WITH_UNANSWERABLE_23_AUG_xlm_roberta-base
QA_SYNTH_DATA_WITH_UNANSWERABLE_23_AUG_xlm_roberta-base 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
- Aug 23, 2023
- Updated
- Aug 23, 2023
- SHA
- 98aed59d
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_DATA_WITH_UNANSWERABLE_23_AUG_xlm_roberta-base
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: 0.0836
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: 2
- eval_batch_size: 2
- 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 |
|---|---|---|---|
| 0.1531 | 1.0 | 5439 | 0.3001 |
| 0.0971 | 2.0 | 10878 | 0.0799 |
| 0.1757 | 3.0 | 16317 | 0.0613 |
| 0.109 | 4.0 | 21756 | 0.0637 |
| 0.0458 | 5.0 | 27195 | 0.0434 |
| 0.0229 | 6.0 | 32634 | 0.0577 |
| 0.0244 | 7.0 | 38073 | 0.0618 |
| 0.0 | 8.0 | 43512 | 0.0814 |
| 0.0 | 9.0 | 48951 | 0.0818 |
| 0.0 | 10.0 | 54390 | 0.0836 |
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
- Aug 23, 2023
- Updated
- Aug 23, 2023