File size: 3,321 Bytes
0827d62 |
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 78 79 80 81 82 83 84 |
---
base_model: bigcode/starencoder
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: stack-edu-classifier-javascript
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. -->
# stack-edu-classifier-javascript
This model is a fine-tuned version of [bigcode/starencoder](https://huggingface.co/bigcode/starencoder) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3612
- Precision: 0.5135
- Recall: 0.3322
- F1 Macro: 0.3711
- Accuracy: 0.6277
- F1 Binary Minimum3: 0.5704
## 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: 0.0003
- train_batch_size: 64
- eval_batch_size: 256
- seed: 0
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 128
- total_eval_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 Macro | Accuracy | F1 Binary Minimum3 |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:--------:|:--------:|:------------------:|
| No log | 0 | 0 | 5.6298 | 0.0010 | 0.1667 | 0.0020 | 0.0059 | 0 |
| 0.3853 | 1.4493 | 1000 | 0.3886 | 0.4945 | 0.3110 | 0.3354 | 0.6037 | 0.5761 |
| 0.3791 | 2.8986 | 2000 | 0.3729 | 0.5041 | 0.3090 | 0.3395 | 0.6208 | 0.5716 |
| 0.3722 | 4.3478 | 3000 | 0.3720 | 0.5261 | 0.3116 | 0.3440 | 0.6189 | 0.5673 |
| 0.3751 | 5.7971 | 4000 | 0.3704 | 0.5247 | 0.3204 | 0.3565 | 0.6199 | 0.5766 |
| 0.3651 | 7.2464 | 5000 | 0.3718 | 0.5113 | 0.3352 | 0.3678 | 0.6310 | 0.5161 |
| 0.3695 | 8.6957 | 6000 | 0.3649 | 0.5055 | 0.3253 | 0.3607 | 0.6249 | 0.5632 |
| 0.361 | 10.1449 | 7000 | 0.3647 | 0.5042 | 0.3236 | 0.3571 | 0.6354 | 0.5410 |
| 0.3666 | 11.5942 | 8000 | 0.3764 | 0.5290 | 0.3371 | 0.3752 | 0.6146 | 0.5941 |
| 0.3563 | 13.0435 | 9000 | 0.3617 | 0.5179 | 0.3356 | 0.3743 | 0.6303 | 0.5674 |
| 0.3735 | 14.4928 | 10000 | 0.3663 | 0.4998 | 0.3423 | 0.3760 | 0.6340 | 0.5320 |
| 0.349 | 15.9420 | 11000 | 0.3616 | 0.5063 | 0.3306 | 0.3681 | 0.6273 | 0.5696 |
| 0.3679 | 17.3913 | 12000 | 0.3632 | 0.5078 | 0.3396 | 0.3786 | 0.6252 | 0.5762 |
| 0.3622 | 18.8406 | 13000 | 0.3612 | 0.5135 | 0.3322 | 0.3711 | 0.6277 | 0.5704 |
### Framework versions
- Transformers 4.43.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|