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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-seed0
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-seed0
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: 0.3882
- Accuracy: 0.9138
- Accuracy Sensor 0: 0.9051
- Auroc Sensor 0: 0.9644
- Accuracy Sensor 1: 0.9165
- Auroc Sensor 1: 0.9454
- Accuracy Sensor 2: 0.9324
- Auroc Sensor 2: 0.9773
- Accuracy Aggregated: 0.9010
- Auroc Aggregated: 0.9670
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-------------------:|:----------------:|
| 0.2891 | 0.9997 | 781 | 0.3732 | 0.8385 | 0.8367 | 0.9076 | 0.8089 | 0.9026 | 0.8669 | 0.9409 | 0.8414 | 0.9123 |
| 0.179 | 1.9994 | 1562 | 0.3287 | 0.8639 | 0.8532 | 0.9392 | 0.8835 | 0.9339 | 0.8852 | 0.9648 | 0.8338 | 0.9430 |
| 0.1181 | 2.9990 | 2343 | 0.2500 | 0.9084 | 0.8967 | 0.9587 | 0.9138 | 0.9382 | 0.9327 | 0.9744 | 0.8906 | 0.9623 |
| 0.0614 | 4.0 | 3125 | 0.3212 | 0.9095 | 0.8961 | 0.9636 | 0.9145 | 0.9457 | 0.9238 | 0.9774 | 0.9036 | 0.9662 |
| 0.0267 | 4.9984 | 3905 | 0.3882 | 0.9138 | 0.9051 | 0.9644 | 0.9165 | 0.9454 | 0.9324 | 0.9773 | 0.9010 | 0.9670 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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