---
license: bsd-3-clause
base_model: Salesforce/codegen-350M-mono
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: codegen-350M-mono-measurement_pred-diamonds-seed6
  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. -->

# codegen-350M-mono-measurement_pred-diamonds-seed6

This model is a fine-tuned version of [Salesforce/codegen-350M-mono](https://huggingface.co/Salesforce/codegen-350M-mono) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2981
- Accuracy: 0.9264
- Accuracy Sensor 0: 0.9256
- Auroc Sensor 0: 0.9730
- Accuracy Sensor 1: 0.9264
- Auroc Sensor 1: 0.9550
- Accuracy Sensor 2: 0.9466
- Auroc Sensor 2: 0.9816
- Accuracy Aggregated: 0.9069
- Auroc Aggregated: 0.9695

## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 64
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Accuracy Sensor 0 | Auroc Sensor 0 | Accuracy Sensor 1 | Auroc Sensor 1 | Accuracy Sensor 2 | Auroc Sensor 2 | Accuracy Aggregated | Auroc Aggregated |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-------------------:|:----------------:|
| 0.2741        | 0.9997 | 781  | 0.2721          | 0.8923   | 0.9024            | 0.9236         | 0.8941            | 0.9249         | 0.9104            | 0.9478         | 0.8624              | 0.9125           |
| 0.1844        | 1.9994 | 1562 | 0.2277          | 0.9106   | 0.9179            | 0.9518         | 0.9016            | 0.9472         | 0.9261            | 0.9696         | 0.8967              | 0.9453           |
| 0.1191        | 2.9990 | 2343 | 0.2076          | 0.9246   | 0.9277            | 0.9671         | 0.9287            | 0.9586         | 0.9424            | 0.9783         | 0.8996              | 0.9638           |
| 0.0703        | 4.0    | 3125 | 0.2424          | 0.9277   | 0.9280            | 0.9723         | 0.9253            | 0.9534         | 0.9423            | 0.9815         | 0.9154              | 0.9686           |
| 0.0353        | 4.9984 | 3905 | 0.2981          | 0.9264   | 0.9256            | 0.9730         | 0.9264            | 0.9550         | 0.9466            | 0.9816         | 0.9069              | 0.9695           |


### Framework versions

- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1