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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-seed1
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-seed1
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: 0.4083
- Accuracy: 0.9134
- Accuracy Sensor 0: 0.9153
- Auroc Sensor 0: 0.9651
- Accuracy Sensor 1: 0.9094
- Auroc Sensor 1: 0.9502
- Accuracy Sensor 2: 0.9317
- Auroc Sensor 2: 0.9780
- Accuracy Aggregated: 0.8974
- Auroc Aggregated: 0.9672
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-------------------:|:----------------:|
| 0.2812 | 0.9997 | 781 | 0.2931 | 0.8747 | 0.8785 | 0.9058 | 0.8806 | 0.9047 | 0.8897 | 0.9331 | 0.8499 | 0.9009 |
| 0.1938 | 1.9994 | 1562 | 0.2940 | 0.8844 | 0.8760 | 0.9330 | 0.9017 | 0.9300 | 0.9160 | 0.9574 | 0.8438 | 0.9252 |
| 0.1202 | 2.9990 | 2343 | 0.2551 | 0.9080 | 0.9055 | 0.9601 | 0.9119 | 0.9504 | 0.9235 | 0.9757 | 0.8910 | 0.9615 |
| 0.0779 | 4.0 | 3125 | 0.2902 | 0.9178 | 0.9194 | 0.9667 | 0.9164 | 0.9516 | 0.9309 | 0.9799 | 0.9044 | 0.9680 |
| 0.035 | 4.9984 | 3905 | 0.4083 | 0.9134 | 0.9153 | 0.9651 | 0.9094 | 0.9502 | 0.9317 | 0.9780 | 0.8974 | 0.9672 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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