---
tags:
- generated_from_trainer
datasets:
- enoriega/odinsynth_dataset
model-index:
- name: rule_learning_margin_1mm_many_negatives_spanpred_attention
  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_many_negatives_spanpred_attention

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: 0.2369
- Margin Accuracy: 0.8923

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|
| 0.3814        | 0.16  | 20   | 0.3909          | 0.8317          |
| 0.349         | 0.32  | 40   | 0.3335          | 0.8463          |
| 0.3196        | 0.48  | 60   | 0.3101          | 0.8587          |
| 0.3083        | 0.64  | 80   | 0.3010          | 0.8645          |
| 0.2828        | 0.8   | 100  | 0.2871          | 0.8686          |
| 0.294         | 0.96  | 120  | 0.2800          | 0.8715          |
| 0.2711        | 1.12  | 140  | 0.2708          | 0.8741          |
| 0.2663        | 1.28  | 160  | 0.2671          | 0.8767          |
| 0.2656        | 1.44  | 180  | 0.2612          | 0.8822          |
| 0.2645        | 1.6   | 200  | 0.2537          | 0.8851          |
| 0.2625        | 1.76  | 220  | 0.2483          | 0.8878          |
| 0.2651        | 1.92  | 240  | 0.2471          | 0.8898          |
| 0.2407        | 2.08  | 260  | 0.2438          | 0.8905          |
| 0.2315        | 2.24  | 280  | 0.2408          | 0.8909          |
| 0.2461        | 2.4   | 300  | 0.2390          | 0.8918          |
| 0.2491        | 2.56  | 320  | 0.2390          | 0.8921          |
| 0.2511        | 2.72  | 340  | 0.2369          | 0.8918          |
| 0.2341        | 2.88  | 360  | 0.2363          | 0.8921          |


### Framework versions

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