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mdeberta-v3-base-finetuned-recores
versae/mdeberta-v3-base-finetuned-recores
mdeberta-v3-base-finetuned-recores is a multiple choice model from versae. Use it for the multiple choice task on the model card, and read the license before you ship it in a product. 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
- multiple choice
- Library
- transformers
- License
- mit
- Model type
- deberta-v2
- Access
- Public
- Created
- May 22, 2022
- Updated
- May 22, 2022
- SHA
- fb8f15c0
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. -->
mdeberta-v3-base-finetuned-recores
This model is a fine-tuned version of microsoft/mdeberta-v3-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.6094
- Accuracy: 0.2011
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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 3000
- num_epochs: 25
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.6112 | 1.0 | 1047 | 1.6094 | 0.1901 |
| 1.608 | 2.0 | 2094 | 1.6094 | 0.1873 |
| 1.6127 | 3.0 | 3141 | 1.6095 | 0.1983 |
| 1.6125 | 4.0 | 4188 | 1.6094 | 0.2424 |
| 1.6118 | 5.0 | 5235 | 1.6094 | 0.1956 |
| 1.6181 | 6.0 | 6282 | 1.6094 | 0.2094 |
| 1.6229 | 7.0 | 7329 | 1.6095 | 0.1680 |
| 1.6125 | 8.0 | 8376 | 1.6094 | 0.1736 |
| 1.6134 | 9.0 | 9423 | 1.6094 | 0.2066 |
| 1.6174 | 10.0 | 10470 | 1.6093 | 0.2204 |
| 1.6161 | 11.0 | 11517 | 1.6096 | 0.2121 |
| 1.6198 | 12.0 | 12564 | 1.6094 | 0.2039 |
| 1.6182 | 13.0 | 13611 | 1.6094 | 0.2287 |
| 1.6208 | 14.0 | 14658 | 1.6094 | 0.2287 |
| 1.6436 | 15.0 | 15705 | 1.6092 | 0.2287 |
| 1.6209 | 16.0 | 16752 | 1.6094 | 0.2094 |
| 1.6097 | 17.0 | 17799 | 1.6094 | 0.2094 |
| 1.6115 | 18.0 | 18846 | 1.6094 | 0.2149 |
| 1.6249 | 19.0 | 19893 | 1.6094 | 0.1956 |
| 1.6201 | 20.0 | 20940 | 1.6094 | 0.1763 |
| 1.6217 | 21.0 | 21987 | 1.6094 | 0.1956 |
| 1.6193 | 22.0 | 23034 | 1.6094 | 0.1846 |
| 1.6171 | 23.0 | 24081 | 1.6095 | 0.1983 |
| 1.6123 | 24.0 | 25128 | 1.6095 | 0.1846 |
| 1.6164 | 25.0 | 26175 | 1.6094 | 0.2011 |
Framework versions
- Transformers 4.19.0
- Pytorch 1.10.1+cu102
- Datasets 2.2.1
- Tokenizers 0.12.1
- Task
- multiple choice
- Library
- transformers
- Type
- deberta-v2
- License
- mit
- Created
- May 22, 2022
- Updated
- May 22, 2022