File size: 3,123 Bytes
aa1a403 adf2788 aa1a403 adf2788 aa1a403 adf2788 aa1a403 adf2788 74860cb aa1a403 adf2788 aa1a403 adf2788 aa1a403 adf2788 aa1a403 adf2788 aa1a403 adf2788 aa1a403 adf2788 aa1a403 adf2788 aa1a403 adf2788 aa1a403 adf2788 aa1a403 adf2788 aa1a403 adf2788 74860cb aa1a403 adf2788 aa1a403 adf2788 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
---
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-seed5
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-seed5
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: 0.5059
- Accuracy: 0.9004
- Accuracy Sensor 0: 0.9014
- Auroc Sensor 0: 0.9556
- Accuracy Sensor 1: 0.8987
- Auroc Sensor 1: 0.9560
- Accuracy Sensor 2: 0.9201
- Auroc Sensor 2: 0.9711
- Accuracy Aggregated: 0.8813
- Auroc Aggregated: 0.9554
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-------------------:|:----------------:|
| 0.2902 | 0.9997 | 781 | 0.3671 | 0.8431 | 0.8573 | 0.9025 | 0.8378 | 0.8994 | 0.8717 | 0.9333 | 0.8057 | 0.8989 |
| 0.1961 | 1.9994 | 1562 | 0.2889 | 0.8868 | 0.8871 | 0.9334 | 0.8813 | 0.9320 | 0.9069 | 0.9565 | 0.8718 | 0.9332 |
| 0.1311 | 2.9990 | 2343 | 0.3433 | 0.8875 | 0.8886 | 0.9531 | 0.8797 | 0.9519 | 0.9099 | 0.9714 | 0.8718 | 0.9536 |
| 0.0788 | 4.0 | 3125 | 0.3613 | 0.9028 | 0.9005 | 0.9566 | 0.9027 | 0.9579 | 0.9199 | 0.9716 | 0.8882 | 0.9555 |
| 0.043 | 4.9984 | 3905 | 0.5059 | 0.9004 | 0.9014 | 0.9556 | 0.8987 | 0.9560 | 0.9201 | 0.9711 | 0.8813 | 0.9554 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|