End of training
Browse files
README.md
ADDED
@@ -0,0 +1,56 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
library_name: transformers
|
3 |
+
license: mit
|
4 |
+
base_model: FacebookAI/xlm-roberta-large
|
5 |
+
tags:
|
6 |
+
- generated_from_trainer
|
7 |
+
model-index:
|
8 |
+
- name: xxx-ner-ghtk-ai-fluent-segmented-21-label-new-data-3090-6Obt-1
|
9 |
+
results: []
|
10 |
+
---
|
11 |
+
|
12 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
13 |
+
should probably proofread and complete it, then remove this comment. -->
|
14 |
+
|
15 |
+
# xxx-ner-ghtk-ai-fluent-segmented-21-label-new-data-3090-6Obt-1
|
16 |
+
|
17 |
+
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
|
18 |
+
|
19 |
+
## Model description
|
20 |
+
|
21 |
+
More information needed
|
22 |
+
|
23 |
+
## Intended uses & limitations
|
24 |
+
|
25 |
+
More information needed
|
26 |
+
|
27 |
+
## Training and evaluation data
|
28 |
+
|
29 |
+
More information needed
|
30 |
+
|
31 |
+
## Training procedure
|
32 |
+
|
33 |
+
### Training hyperparameters
|
34 |
+
|
35 |
+
The following hyperparameters were used during training:
|
36 |
+
- learning_rate: 2.5e-05
|
37 |
+
- train_batch_size: 8
|
38 |
+
- eval_batch_size: 8
|
39 |
+
- seed: 42
|
40 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
41 |
+
- lr_scheduler_type: linear
|
42 |
+
- num_epochs: 1
|
43 |
+
|
44 |
+
### Training results
|
45 |
+
|
46 |
+
| Training Loss | Epoch | Step | Validation Loss | Ho | Hoảng thời gian | Háng trừu tượng | Hông tin ctt | Hụ cấp | Hứ | Iấy tờ | Iền cụ thể | Iền trừu tượng | Ã số thuế | Ã đơn | Ình thức làm việc | Ông | Ương | Ị trí | Ố công | Ố giờ | Ố điểm | Ố đơn | Ợt | Ỷ lệ | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|
47 |
+
|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:----------------------------------------------------------:|:---------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:----------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:----------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------:|:----------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:----------------------------------------------------------:|:----------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
|
48 |
+
| No log | 1.0 | 147 | 0.5842 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} | {'precision': 0.19148936170212766, 'recall': 0.14285714285714285, 'f1': 0.16363636363636364, 'number': 63} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 31} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 22} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.07079646017699115, 'recall': 0.0975609756097561, 'f1': 0.08205128205128205, 'number': 82} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 54} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 16} | {'precision': 0.6938775510204082, 'recall': 0.5551020408163265, 'f1': 0.6167800453514739, 'number': 245} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 50} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 27} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | 0.3825 | 0.2361 | 0.2920 | 0.8515 |
|
49 |
+
|
50 |
+
|
51 |
+
### Framework versions
|
52 |
+
|
53 |
+
- Transformers 4.44.2
|
54 |
+
- Pytorch 2.4.1+cu121
|
55 |
+
- Datasets 3.0.0
|
56 |
+
- Tokenizers 0.19.1
|