---
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: 0.4189
- Accuracy: 0.9210
- Accuracy Sensor 0: 0.9298
- Auroc Sensor 0: 0.9628
- Accuracy Sensor 1: 0.9259
- Auroc Sensor 1: 0.9711
- Accuracy Sensor 2: 0.9266
- Auroc Sensor 2: 0.9619
- Accuracy Aggregated: 0.9019
- Auroc Aggregated: 0.9592

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-------------------:|:----------------:|
| 0.2961        | 0.9997 | 781  | 0.4800          | 0.7906   | 0.8122            | 0.9078         | 0.7952            | 0.9255         | 0.8160            | 0.9280         | 0.7391              | 0.8990           |
| 0.1901        | 1.9994 | 1562 | 0.3107          | 0.8847   | 0.9115            | 0.9491         | 0.8649            | 0.9604         | 0.8951            | 0.9532         | 0.8674              | 0.9397           |
| 0.1154        | 2.9990 | 2343 | 0.3076          | 0.9009   | 0.9154            | 0.9575         | 0.8946            | 0.9656         | 0.9255            | 0.9576         | 0.8682              | 0.9492           |
| 0.0708        | 4.0    | 3125 | 0.3162          | 0.9207   | 0.9297            | 0.9621         | 0.9245            | 0.9710         | 0.9285            | 0.9619         | 0.9001              | 0.9587           |
| 0.0314        | 4.9984 | 3905 | 0.4189          | 0.9210   | 0.9298            | 0.9628         | 0.9259            | 0.9711         | 0.9266            | 0.9619         | 0.9019              | 0.9592           |


### Framework versions

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