---
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-seed1
  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-seed1

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: 0.4083
- Accuracy: 0.9134
- Accuracy Sensor 0: 0.9153
- Auroc Sensor 0: 0.9651
- Accuracy Sensor 1: 0.9094
- Auroc Sensor 1: 0.9502
- Accuracy Sensor 2: 0.9317
- Auroc Sensor 2: 0.9780
- Accuracy Aggregated: 0.8974
- Auroc Aggregated: 0.9672

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-------------------:|:----------------:|
| 0.2812        | 0.9997 | 781  | 0.2931          | 0.8747   | 0.8785            | 0.9058         | 0.8806            | 0.9047         | 0.8897            | 0.9331         | 0.8499              | 0.9009           |
| 0.1938        | 1.9994 | 1562 | 0.2940          | 0.8844   | 0.8760            | 0.9330         | 0.9017            | 0.9300         | 0.9160            | 0.9574         | 0.8438              | 0.9252           |
| 0.1202        | 2.9990 | 2343 | 0.2551          | 0.9080   | 0.9055            | 0.9601         | 0.9119            | 0.9504         | 0.9235            | 0.9757         | 0.8910              | 0.9615           |
| 0.0779        | 4.0    | 3125 | 0.2902          | 0.9178   | 0.9194            | 0.9667         | 0.9164            | 0.9516         | 0.9309            | 0.9799         | 0.9044              | 0.9680           |
| 0.035         | 4.9984 | 3905 | 0.4083          | 0.9134   | 0.9153            | 0.9651         | 0.9094            | 0.9502         | 0.9317            | 0.9780         | 0.8974              | 0.9672           |


### Framework versions

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