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QA_SYNTH_02_OCT_WITH_FINETUNE_1.1
am-infoweb/QA_SYNTH_02_OCT_WITH_FINETUNE_1.1
QA_SYNTH_02_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 2, 2023
- Updated
- Oct 2, 2023
- SHA
- a32cc326
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_02_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: 0.0018
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 |
|---|---|---|---|
| 0.3977 | 1.0 | 7341 | 0.0353 |
| 0.0567 | 2.0 | 14682 | 0.0191 |
| 0.0691 | 3.0 | 22023 | 0.0517 |
| 0.0152 | 4.0 | 29364 | 0.0132 |
| 0.0191 | 5.0 | 36705 | 0.0620 |
| 0.0023 | 6.0 | 44046 | 0.0091 |
| 0.0004 | 7.0 | 51387 | 0.0044 |
| 0.0047 | 8.0 | 58728 | 0.0053 |
| 0.0003 | 9.0 | 66069 | 0.0018 |
| 0.0 | 10.0 | 73410 | 0.0018 |
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 2, 2023
- Updated
- Oct 2, 2023