---
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-seed2
  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-seed2

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: 0.4023
- Accuracy: 0.9108
- Accuracy Sensor 0: 0.9220
- Auroc Sensor 0: 0.9580
- Accuracy Sensor 1: 0.9109
- Auroc Sensor 1: 0.9645
- Accuracy Sensor 2: 0.9260
- Auroc Sensor 2: 0.9611
- Accuracy Aggregated: 0.8845
- Auroc Aggregated: 0.9532

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-------------------:|:----------------:|
| 0.3009        | 0.9997 | 781  | 0.4552          | 0.8074   | 0.8220            | 0.9041         | 0.8092            | 0.9255         | 0.8372            | 0.9304         | 0.7610              | 0.9026           |
| 0.1989        | 1.9994 | 1562 | 0.3633          | 0.8595   | 0.8835            | 0.9425         | 0.8544            | 0.9520         | 0.8757            | 0.9517         | 0.8244              | 0.9351           |
| 0.1335        | 2.9990 | 2343 | 0.3032          | 0.8924   | 0.8985            | 0.9529         | 0.8877            | 0.9608         | 0.9246            | 0.9573         | 0.8588              | 0.9463           |
| 0.093         | 4.0    | 3125 | 0.3016          | 0.9138   | 0.9203            | 0.9581         | 0.9131            | 0.9651         | 0.9304            | 0.9609         | 0.8914              | 0.9529           |
| 0.0432        | 4.9984 | 3905 | 0.4023          | 0.9108   | 0.9220            | 0.9580         | 0.9109            | 0.9645         | 0.9260            | 0.9611         | 0.8845              | 0.9532           |


### Framework versions

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