---
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: 0.3448
- Accuracy: 0.9221
- Accuracy Sensor 0: 0.9220
- Auroc Sensor 0: 0.9685
- Accuracy Sensor 1: 0.9246
- Auroc Sensor 1: 0.9499
- Accuracy Sensor 2: 0.9424
- Auroc Sensor 2: 0.9773
- Accuracy Aggregated: 0.8995
- Auroc Aggregated: 0.9623

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-------------------:|:----------------:|
| 0.2903        | 0.9997 | 781  | 0.2948          | 0.8787   | 0.8929            | 0.9140         | 0.8837            | 0.9156         | 0.8940            | 0.9364         | 0.8443              | 0.9039           |
| 0.1957        | 1.9994 | 1562 | 0.2496          | 0.8951   | 0.9185            | 0.9448         | 0.8936            | 0.9374         | 0.9065            | 0.9627         | 0.8617              | 0.9375           |
| 0.1345        | 2.9990 | 2343 | 0.2076          | 0.9244   | 0.9279            | 0.9643         | 0.9283            | 0.9521         | 0.9358            | 0.9756         | 0.9056              | 0.9583           |
| 0.0781        | 4.0    | 3125 | 0.2624          | 0.9256   | 0.9263            | 0.9678         | 0.9256            | 0.9521         | 0.9388            | 0.9775         | 0.9116              | 0.9611           |
| 0.0384        | 4.9984 | 3905 | 0.3448          | 0.9221   | 0.9220            | 0.9685         | 0.9246            | 0.9499         | 0.9424            | 0.9773         | 0.8995              | 0.9623           |


### Framework versions

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