---
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-seed7
  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-seed7

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: 0.5040
- Accuracy: 0.9090
- Accuracy Sensor 0: 0.9133
- Auroc Sensor 0: 0.9558
- Accuracy Sensor 1: 0.9094
- Auroc Sensor 1: 0.9574
- Accuracy Sensor 2: 0.9209
- Auroc Sensor 2: 0.9484
- Accuracy Aggregated: 0.8924
- Auroc Aggregated: 0.9451

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-------------------:|:----------------:|
| 0.292         | 0.9997 | 781  | 0.4645          | 0.7965   | 0.7764            | 0.9029         | 0.7966            | 0.9121         | 0.8383            | 0.9100         | 0.7747              | 0.8919           |
| 0.1979        | 1.9994 | 1562 | 0.3658          | 0.8561   | 0.8608            | 0.9319         | 0.8325            | 0.9368         | 0.8895            | 0.9374         | 0.8415              | 0.9235           |
| 0.1187        | 2.9990 | 2343 | 0.3611          | 0.8739   | 0.8911            | 0.9495         | 0.8882            | 0.9516         | 0.8664            | 0.9434         | 0.8497              | 0.9367           |
| 0.0666        | 4.0    | 3125 | 0.3757          | 0.9075   | 0.9064            | 0.9566         | 0.9149            | 0.9599         | 0.9146            | 0.9481         | 0.8942              | 0.9454           |
| 0.0267        | 4.9984 | 3905 | 0.5040          | 0.9090   | 0.9133            | 0.9558         | 0.9094            | 0.9574         | 0.9209            | 0.9484         | 0.8924              | 0.9451           |


### Framework versions

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