---
license: mit
base_model: avsolatorio/GIST-large-Embedding-v0
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: chefdiff
  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. -->

# chefdiff

This model is a fine-tuned version of [avsolatorio/GIST-large-Embedding-v0](https://huggingface.co./avsolatorio/GIST-large-Embedding-v0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2966
- F1: 0.6367
- Roc Auc: 0.7929
- Accuracy: 0.0909

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 35

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.4056        | 1.0   | 50   | 0.3591          | 0.1786 | 0.5651  | 0.0182   |
| 0.3103        | 2.0   | 100  | 0.3197          | 0.3214 | 0.6256  | 0.0909   |
| 0.2522        | 3.0   | 150  | 0.2939          | 0.3551 | 0.6471  | 0.0727   |
| 0.2039        | 4.0   | 200  | 0.2741          | 0.4813 | 0.7092  | 0.1273   |
| 0.1632        | 5.0   | 250  | 0.2714          | 0.5493 | 0.7380  | 0.0909   |
| 0.1308        | 6.0   | 300  | 0.2532          | 0.4740 | 0.6994  | 0.1636   |
| 0.1056        | 7.0   | 350  | 0.2644          | 0.5641 | 0.7575  | 0.0909   |
| 0.0837        | 8.0   | 400  | 0.2541          | 0.5684 | 0.7579  | 0.1091   |
| 0.0679        | 9.0   | 450  | 0.2542          | 0.6112 | 0.7782  | 0.1273   |
| 0.0562        | 10.0  | 500  | 0.2587          | 0.6342 | 0.7932  | 0.1455   |
| 0.0476        | 11.0  | 550  | 0.2547          | 0.6399 | 0.7913  | 0.1455   |
| 0.0421        | 12.0  | 600  | 0.2683          | 0.6429 | 0.7998  | 0.1273   |
| 0.0369        | 13.0  | 650  | 0.2738          | 0.6146 | 0.7839  | 0.0909   |
| 0.0335        | 14.0  | 700  | 0.2678          | 0.6387 | 0.7920  | 0.0909   |
| 0.0298        | 15.0  | 750  | 0.2700          | 0.6123 | 0.7812  | 0.0909   |
| 0.0275        | 16.0  | 800  | 0.2737          | 0.6152 | 0.7804  | 0.0909   |
| 0.0255        | 17.0  | 850  | 0.2741          | 0.6339 | 0.7852  | 0.1091   |
| 0.024         | 18.0  | 900  | 0.2778          | 0.6564 | 0.8052  | 0.1091   |
| 0.0223        | 19.0  | 950  | 0.2815          | 0.6521 | 0.8019  | 0.1091   |
| 0.0213        | 20.0  | 1000 | 0.2778          | 0.6296 | 0.7902  | 0.0727   |
| 0.0201        | 21.0  | 1050 | 0.2861          | 0.6340 | 0.7900  | 0.0909   |
| 0.0192        | 22.0  | 1100 | 0.2819          | 0.6413 | 0.7949  | 0.0909   |
| 0.0185        | 23.0  | 1150 | 0.2913          | 0.6280 | 0.7888  | 0.0727   |
| 0.0178        | 24.0  | 1200 | 0.2874          | 0.6347 | 0.7938  | 0.0727   |
| 0.0172        | 25.0  | 1250 | 0.2890          | 0.6271 | 0.7879  | 0.0909   |
| 0.0166        | 26.0  | 1300 | 0.2900          | 0.6306 | 0.7901  | 0.0727   |
| 0.0161        | 27.0  | 1350 | 0.2944          | 0.6284 | 0.7896  | 0.0727   |
| 0.0158        | 28.0  | 1400 | 0.2931          | 0.6407 | 0.7954  | 0.0909   |
| 0.0154        | 29.0  | 1450 | 0.2942          | 0.6345 | 0.7937  | 0.0727   |
| 0.015         | 30.0  | 1500 | 0.2960          | 0.6413 | 0.7942  | 0.0909   |
| 0.0147        | 31.0  | 1550 | 0.2927          | 0.6477 | 0.7966  | 0.0909   |
| 0.0147        | 32.0  | 1600 | 0.2952          | 0.6396 | 0.7943  | 0.0909   |
| 0.0145        | 33.0  | 1650 | 0.2965          | 0.6314 | 0.7924  | 0.0727   |
| 0.0144        | 34.0  | 1700 | 0.2971          | 0.6367 | 0.7929  | 0.0909   |
| 0.0143        | 35.0  | 1750 | 0.2966          | 0.6367 | 0.7929  | 0.0909   |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2