---
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