---
tags:
- generated_from_trainer
datasets:
- enoriega/odinsynth_dataset
model-index:
- name: rule_learning_margin_1mm_spanpred
  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_margin_1mm_spanpred

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: 0.3250
- Margin Accuracy: 0.8518

## 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 | Margin Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|
| 0.5448        | 0.16  | 20   | 0.5229          | 0.7717          |
| 0.4571        | 0.32  | 40   | 0.4292          | 0.8109          |
| 0.4296        | 0.48  | 60   | 0.4009          | 0.8193          |
| 0.4028        | 0.64  | 80   | 0.3855          | 0.8296          |
| 0.3878        | 0.8   | 100  | 0.3757          | 0.8334          |
| 0.3831        | 0.96  | 120  | 0.3643          | 0.8367          |
| 0.3591        | 1.12  | 140  | 0.3582          | 0.8393          |
| 0.3598        | 1.28  | 160  | 0.3533          | 0.8401          |
| 0.3635        | 1.44  | 180  | 0.3442          | 0.8427          |
| 0.3478        | 1.6   | 200  | 0.3406          | 0.8472          |
| 0.342         | 1.76  | 220  | 0.3352          | 0.8479          |
| 0.3327        | 1.92  | 240  | 0.3352          | 0.8486          |
| 0.3487        | 2.08  | 260  | 0.3293          | 0.8487          |
| 0.3387        | 2.24  | 280  | 0.3298          | 0.8496          |
| 0.3457        | 2.4   | 300  | 0.3279          | 0.8505          |
| 0.3483        | 2.56  | 320  | 0.3286          | 0.8510          |
| 0.3421        | 2.72  | 340  | 0.3245          | 0.8517          |
| 0.3332        | 2.88  | 360  | 0.3252          | 0.8517          |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.1
- Tokenizers 0.12.1