Model save
Browse files
README.md
ADDED
|
@@ -0,0 +1,79 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: apache-2.0
|
| 3 |
+
base_model: facebook/convnextv2-tiny-22k-384
|
| 4 |
+
tags:
|
| 5 |
+
- generated_from_trainer
|
| 6 |
+
metrics:
|
| 7 |
+
- accuracy
|
| 8 |
+
- precision
|
| 9 |
+
- recall
|
| 10 |
+
- f1
|
| 11 |
+
model-index:
|
| 12 |
+
- name: 10-finetuned-spiderTraining50-200
|
| 13 |
+
results: []
|
| 14 |
+
---
|
| 15 |
+
|
| 16 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
| 17 |
+
should probably proofread and complete it, then remove this comment. -->
|
| 18 |
+
|
| 19 |
+
# 10-finetuned-spiderTraining50-200
|
| 20 |
+
|
| 21 |
+
This model is a fine-tuned version of [facebook/convnextv2-tiny-22k-384](https://huggingface.co/facebook/convnextv2-tiny-22k-384) on an unknown dataset.
|
| 22 |
+
It achieves the following results on the evaluation set:
|
| 23 |
+
- Loss: 0.3858
|
| 24 |
+
- Accuracy: 0.8899
|
| 25 |
+
- Precision: 0.8906
|
| 26 |
+
- Recall: 0.8904
|
| 27 |
+
- F1: 0.8882
|
| 28 |
+
|
| 29 |
+
## Model description
|
| 30 |
+
|
| 31 |
+
More information needed
|
| 32 |
+
|
| 33 |
+
## Intended uses & limitations
|
| 34 |
+
|
| 35 |
+
More information needed
|
| 36 |
+
|
| 37 |
+
## Training and evaluation data
|
| 38 |
+
|
| 39 |
+
More information needed
|
| 40 |
+
|
| 41 |
+
## Training procedure
|
| 42 |
+
|
| 43 |
+
### Training hyperparameters
|
| 44 |
+
|
| 45 |
+
The following hyperparameters were used during training:
|
| 46 |
+
- learning_rate: 0.0005
|
| 47 |
+
- train_batch_size: 16
|
| 48 |
+
- eval_batch_size: 16
|
| 49 |
+
- seed: 42
|
| 50 |
+
- distributed_type: multi-GPU
|
| 51 |
+
- gradient_accumulation_steps: 4
|
| 52 |
+
- total_train_batch_size: 64
|
| 53 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
| 54 |
+
- lr_scheduler_type: linear
|
| 55 |
+
- lr_scheduler_warmup_ratio: 0.1
|
| 56 |
+
- num_epochs: 10
|
| 57 |
+
|
| 58 |
+
### Training results
|
| 59 |
+
|
| 60 |
+
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|
| 61 |
+
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
|
| 62 |
+
| 1.6006 | 1.0 | 125 | 1.3292 | 0.6246 | 0.6998 | 0.6230 | 0.6169 |
|
| 63 |
+
| 1.3122 | 2.0 | 250 | 1.0785 | 0.6747 | 0.7123 | 0.6769 | 0.6664 |
|
| 64 |
+
| 0.8869 | 3.0 | 375 | 0.9295 | 0.7367 | 0.7838 | 0.7398 | 0.7424 |
|
| 65 |
+
| 0.7394 | 4.0 | 500 | 0.6719 | 0.8078 | 0.8157 | 0.8089 | 0.8041 |
|
| 66 |
+
| 0.5704 | 5.0 | 625 | 0.6200 | 0.8198 | 0.8320 | 0.8265 | 0.8176 |
|
| 67 |
+
| 0.4273 | 6.0 | 750 | 0.5874 | 0.8338 | 0.8469 | 0.8338 | 0.8310 |
|
| 68 |
+
| 0.3289 | 7.0 | 875 | 0.5009 | 0.8549 | 0.8618 | 0.8523 | 0.8519 |
|
| 69 |
+
| 0.2828 | 8.0 | 1000 | 0.4697 | 0.8699 | 0.8728 | 0.8696 | 0.8677 |
|
| 70 |
+
| 0.2991 | 9.0 | 1125 | 0.4464 | 0.8639 | 0.8651 | 0.8653 | 0.8609 |
|
| 71 |
+
| 0.2108 | 10.0 | 1250 | 0.3858 | 0.8899 | 0.8906 | 0.8904 | 0.8882 |
|
| 72 |
+
|
| 73 |
+
|
| 74 |
+
### Framework versions
|
| 75 |
+
|
| 76 |
+
- Transformers 4.33.3
|
| 77 |
+
- Pytorch 2.0.1+cu117
|
| 78 |
+
- Datasets 2.14.5
|
| 79 |
+
- Tokenizers 0.13.3
|