File size: 3,123 Bytes
995430d 897efbf 995430d 897efbf 995430d |
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-seed7
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-seed7
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: 0.5040
- Accuracy: 0.9090
- Accuracy Sensor 0: 0.9133
- Auroc Sensor 0: 0.9558
- Accuracy Sensor 1: 0.9094
- Auroc Sensor 1: 0.9574
- Accuracy Sensor 2: 0.9209
- Auroc Sensor 2: 0.9484
- Accuracy Aggregated: 0.8924
- Auroc Aggregated: 0.9451
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-------------------:|:----------------:|
| 0.292 | 0.9997 | 781 | 0.4645 | 0.7965 | 0.7764 | 0.9029 | 0.7966 | 0.9121 | 0.8383 | 0.9100 | 0.7747 | 0.8919 |
| 0.1979 | 1.9994 | 1562 | 0.3658 | 0.8561 | 0.8608 | 0.9319 | 0.8325 | 0.9368 | 0.8895 | 0.9374 | 0.8415 | 0.9235 |
| 0.1187 | 2.9990 | 2343 | 0.3611 | 0.8739 | 0.8911 | 0.9495 | 0.8882 | 0.9516 | 0.8664 | 0.9434 | 0.8497 | 0.9367 |
| 0.0666 | 4.0 | 3125 | 0.3757 | 0.9075 | 0.9064 | 0.9566 | 0.9149 | 0.9599 | 0.9146 | 0.9481 | 0.8942 | 0.9454 |
| 0.0267 | 4.9984 | 3905 | 0.5040 | 0.9090 | 0.9133 | 0.9558 | 0.9094 | 0.9574 | 0.9209 | 0.9484 | 0.8924 | 0.9451 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|