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rule_learning_1mm_many_negatives_spanpred_mse_attention
enoriega/rule_learning_1mm_many_negatives_spanpred_mse_attention
rule_learning_1mm_many_negatives_spanpred_mse_attention is a machine learning model from enoriega. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Jul 21, 2022
- Updated
- Jul 22, 2022
- SHA
- 10687756
Datasets
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. -->
rule_learning_1mm_many_negatives_spanpred_avf
This model is a fine-tuned version of enoriega/rule_softmatching on the enoriega/odinsynth_dataset dataset. It achieves the following results on the evaluation set:
- Loss: 0.0731
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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2000
- total_train_batch_size: 8000
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.1215 | 0.16 | 20 | 0.1191 |
| 0.1091 | 0.32 | 40 | 0.1079 |
| 0.0993 | 0.48 | 60 | 0.0993 |
| 0.0938 | 0.64 | 80 | 0.0952 |
| 0.085 | 0.8 | 100 | 0.0858 |
| 0.0837 | 0.96 | 120 | 0.0842 |
| 0.0811 | 1.12 | 140 | 0.0827 |
| 0.0799 | 1.28 | 160 | 0.0809 |
| 0.078 | 1.44 | 180 | 0.0786 |
| 0.0792 | 1.6 | 200 | 0.0781 |
| 0.0797 | 1.76 | 220 | 0.0765 |
| 0.0775 | 1.92 | 240 | 0.0758 |
| 0.0735 | 2.08 | 260 | 0.0748 |
| 0.0704 | 2.24 | 280 | 0.0744 |
| 0.0744 | 2.4 | 300 | 0.0737 |
| 0.0752 | 2.56 | 320 | 0.0733 |
| 0.075 | 2.72 | 340 | 0.0738 |
| 0.0701 | 2.88 | 360 | 0.0732 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.1
- Tokenizers 0.12.1
- Library
- transformers
- Type
- bert
- Created
- Jul 21, 2022
- Updated
- Jul 22, 2022