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---
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
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