Model save
Browse files
README.md
ADDED
|
@@ -0,0 +1,79 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: apache-2.0
|
| 3 |
+
base_model: facebook/convnextv2-base-22k-384
|
| 4 |
+
tags:
|
| 5 |
+
- generated_from_trainer
|
| 6 |
+
metrics:
|
| 7 |
+
- accuracy
|
| 8 |
+
- precision
|
| 9 |
+
- recall
|
| 10 |
+
- f1
|
| 11 |
+
model-index:
|
| 12 |
+
- name: 10-convnextv2-base-22k-384-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-convnextv2-base-22k-384-finetuned-spiderTraining50-200
|
| 20 |
+
|
| 21 |
+
This model is a fine-tuned version of [facebook/convnextv2-base-22k-384](https://huggingface.co/facebook/convnextv2-base-22k-384) on an unknown dataset.
|
| 22 |
+
It achieves the following results on the evaluation set:
|
| 23 |
+
- Loss: 0.2953
|
| 24 |
+
- Accuracy: 0.9179
|
| 25 |
+
- Precision: 0.9143
|
| 26 |
+
- Recall: 0.9169
|
| 27 |
+
- F1: 0.9135
|
| 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: 27
|
| 48 |
+
- eval_batch_size: 27
|
| 49 |
+
- seed: 42
|
| 50 |
+
- distributed_type: multi-GPU
|
| 51 |
+
- gradient_accumulation_steps: 4
|
| 52 |
+
- total_train_batch_size: 108
|
| 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 |
+
| 1.2896 | 1.0 | 74 | 0.9797 | 0.7057 | 0.7360 | 0.7011 | 0.6843 |
|
| 63 |
+
| 1.1398 | 1.99 | 148 | 0.9558 | 0.7227 | 0.7691 | 0.7254 | 0.7197 |
|
| 64 |
+
| 0.7899 | 2.99 | 222 | 0.6987 | 0.7948 | 0.8101 | 0.7890 | 0.7866 |
|
| 65 |
+
| 0.5357 | 4.0 | 297 | 0.6526 | 0.8148 | 0.8327 | 0.8161 | 0.8104 |
|
| 66 |
+
| 0.4807 | 5.0 | 371 | 0.5543 | 0.8398 | 0.8512 | 0.8407 | 0.8367 |
|
| 67 |
+
| 0.3575 | 5.99 | 445 | 0.4465 | 0.8789 | 0.8814 | 0.8790 | 0.8746 |
|
| 68 |
+
| 0.3728 | 6.99 | 519 | 0.4344 | 0.8819 | 0.8840 | 0.8794 | 0.8772 |
|
| 69 |
+
| 0.2892 | 8.0 | 594 | 0.3911 | 0.8859 | 0.8879 | 0.8839 | 0.8804 |
|
| 70 |
+
| 0.2082 | 9.0 | 668 | 0.3256 | 0.9079 | 0.9067 | 0.9091 | 0.9053 |
|
| 71 |
+
| 0.1737 | 9.97 | 740 | 0.2953 | 0.9179 | 0.9143 | 0.9169 | 0.9135 |
|
| 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
|