File size: 2,336 Bytes
6a7c3da 1068775 6a7c3da 1068775 6a7c3da 1068775 6a7c3da |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
---
tags:
- generated_from_trainer
datasets:
- enoriega/odinsynth_dataset
model-index:
- name: rule_learning_1mm_many_negatives_spanpred_avf
results: []
---
<!-- 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](https://huggingface.co/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
|