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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-seed4
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-seed4
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: 0.3733
- Accuracy: 0.9086
- Accuracy Sensor 0: 0.9144
- Auroc Sensor 0: 0.9506
- Accuracy Sensor 1: 0.9050
- Auroc Sensor 1: 0.9584
- Accuracy Sensor 2: 0.9332
- Auroc Sensor 2: 0.9753
- Accuracy Aggregated: 0.8820
- Auroc Aggregated: 0.9557
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-------------------:|:----------------:|
| 0.3029 | 0.9997 | 781 | 0.3411 | 0.8441 | 0.8553 | 0.9103 | 0.8390 | 0.9066 | 0.8633 | 0.9334 | 0.8188 | 0.8975 |
| 0.2003 | 1.9994 | 1562 | 0.2859 | 0.8852 | 0.8929 | 0.9380 | 0.8778 | 0.9380 | 0.9319 | 0.9638 | 0.8384 | 0.9361 |
| 0.1366 | 2.9990 | 2343 | 0.2701 | 0.8945 | 0.9041 | 0.9549 | 0.8902 | 0.9570 | 0.9245 | 0.9755 | 0.8591 | 0.9539 |
| 0.0812 | 4.0 | 3125 | 0.2992 | 0.9046 | 0.9166 | 0.9542 | 0.8947 | 0.9585 | 0.9339 | 0.9765 | 0.8730 | 0.9567 |
| 0.0381 | 4.9984 | 3905 | 0.3733 | 0.9086 | 0.9144 | 0.9506 | 0.9050 | 0.9584 | 0.9332 | 0.9753 | 0.8820 | 0.9557 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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