---
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-seed0
  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-seed0

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: 0.3882
- Accuracy: 0.9138
- Accuracy Sensor 0: 0.9051
- Auroc Sensor 0: 0.9644
- Accuracy Sensor 1: 0.9165
- Auroc Sensor 1: 0.9454
- Accuracy Sensor 2: 0.9324
- Auroc Sensor 2: 0.9773
- Accuracy Aggregated: 0.9010
- Auroc Aggregated: 0.9670

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-------------------:|:----------------:|
| 0.2891        | 0.9997 | 781  | 0.3732          | 0.8385   | 0.8367            | 0.9076         | 0.8089            | 0.9026         | 0.8669            | 0.9409         | 0.8414              | 0.9123           |
| 0.179         | 1.9994 | 1562 | 0.3287          | 0.8639   | 0.8532            | 0.9392         | 0.8835            | 0.9339         | 0.8852            | 0.9648         | 0.8338              | 0.9430           |
| 0.1181        | 2.9990 | 2343 | 0.2500          | 0.9084   | 0.8967            | 0.9587         | 0.9138            | 0.9382         | 0.9327            | 0.9744         | 0.8906              | 0.9623           |
| 0.0614        | 4.0    | 3125 | 0.3212          | 0.9095   | 0.8961            | 0.9636         | 0.9145            | 0.9457         | 0.9238            | 0.9774         | 0.9036              | 0.9662           |
| 0.0267        | 4.9984 | 3905 | 0.3882          | 0.9138   | 0.9051            | 0.9644         | 0.9165            | 0.9454         | 0.9324            | 0.9773         | 0.9010              | 0.9670           |


### Framework versions

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