---
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-seed4
  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-seed4

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: 0.3745
- Accuracy: 0.9126
- Accuracy Sensor 0: 0.9165
- Auroc Sensor 0: 0.9601
- Accuracy Sensor 1: 0.9099
- Auroc Sensor 1: 0.9647
- Accuracy Sensor 2: 0.9342
- Auroc Sensor 2: 0.9771
- Accuracy Aggregated: 0.8898
- Auroc Aggregated: 0.9613

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-------------------:|:----------------:|
| 0.2756        | 0.9997 | 781  | 0.3221          | 0.8643   | 0.8659            | 0.9177         | 0.8499            | 0.9112         | 0.9025            | 0.9476         | 0.8388              | 0.9090           |
| 0.1793        | 1.9994 | 1562 | 0.2547          | 0.8960   | 0.9032            | 0.9461         | 0.8847            | 0.9450         | 0.9345            | 0.9710         | 0.8617              | 0.9433           |
| 0.1281        | 2.9990 | 2343 | 0.2960          | 0.8797   | 0.8882            | 0.9563         | 0.8726            | 0.9584         | 0.9133            | 0.9719         | 0.8447              | 0.9553           |
| 0.0685        | 4.0    | 3125 | 0.3088          | 0.9049   | 0.9163            | 0.9597         | 0.9014            | 0.9638         | 0.9259            | 0.9765         | 0.8761              | 0.9609           |
| 0.0342        | 4.9984 | 3905 | 0.3745          | 0.9126   | 0.9165            | 0.9601         | 0.9099            | 0.9647         | 0.9342            | 0.9771         | 0.8898              | 0.9613           |


### Framework versions

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