metadata

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: []

codegen-350M-mono-measurement_pred-diamonds-seed2

This model is a fine-tuned version of 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