update model card README.md
Browse files
README.md
ADDED
@@ -0,0 +1,73 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
tags:
|
3 |
+
- generated_from_trainer
|
4 |
+
metrics:
|
5 |
+
- precision
|
6 |
+
- recall
|
7 |
+
- f1
|
8 |
+
- accuracy
|
9 |
+
model-index:
|
10 |
+
- name: AraBERT_token_classification__AraEval24_truncated_rand
|
11 |
+
results: []
|
12 |
+
---
|
13 |
+
|
14 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
15 |
+
should probably proofread and complete it, then remove this comment. -->
|
16 |
+
|
17 |
+
# AraBERT_token_classification__AraEval24_truncated_rand
|
18 |
+
|
19 |
+
This model is a fine-tuned version of [aubmindlab/bert-base-arabert](https://huggingface.co/aubmindlab/bert-base-arabert) on the None dataset.
|
20 |
+
It achieves the following results on the evaluation set:
|
21 |
+
- Loss: 2.0021
|
22 |
+
- Precision: 0.1263
|
23 |
+
- Recall: 0.1171
|
24 |
+
- F1: 0.1215
|
25 |
+
- Accuracy: 0.5544
|
26 |
+
|
27 |
+
## Model description
|
28 |
+
|
29 |
+
More information needed
|
30 |
+
|
31 |
+
## Intended uses & limitations
|
32 |
+
|
33 |
+
More information needed
|
34 |
+
|
35 |
+
## Training and evaluation data
|
36 |
+
|
37 |
+
More information needed
|
38 |
+
|
39 |
+
## Training procedure
|
40 |
+
|
41 |
+
### Training hyperparameters
|
42 |
+
|
43 |
+
The following hyperparameters were used during training:
|
44 |
+
- learning_rate: 2e-05
|
45 |
+
- train_batch_size: 8
|
46 |
+
- eval_batch_size: 8
|
47 |
+
- seed: 42
|
48 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
49 |
+
- lr_scheduler_type: linear
|
50 |
+
- num_epochs: 10
|
51 |
+
|
52 |
+
### Training results
|
53 |
+
|
54 |
+
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|
55 |
+
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
|
56 |
+
| No log | 1.0 | 479 | 1.7309 | 0.0864 | 0.0189 | 0.0310 | 0.5847 |
|
57 |
+
| 1.6653 | 2.0 | 958 | 1.6503 | 0.0855 | 0.0393 | 0.0539 | 0.5793 |
|
58 |
+
| 1.3608 | 3.0 | 1437 | 1.6761 | 0.1075 | 0.0579 | 0.0753 | 0.5869 |
|
59 |
+
| 1.1267 | 4.0 | 1916 | 1.7633 | 0.1003 | 0.0786 | 0.0882 | 0.5442 |
|
60 |
+
| 0.9119 | 5.0 | 2395 | 1.7995 | 0.1050 | 0.0877 | 0.0956 | 0.5442 |
|
61 |
+
| 0.783 | 6.0 | 2874 | 1.8613 | 0.1151 | 0.0937 | 0.1033 | 0.5607 |
|
62 |
+
| 0.6667 | 7.0 | 3353 | 1.9148 | 0.1155 | 0.1061 | 0.1106 | 0.5472 |
|
63 |
+
| 0.5967 | 8.0 | 3832 | 1.9480 | 0.1267 | 0.1175 | 0.1219 | 0.5511 |
|
64 |
+
| 0.5397 | 9.0 | 4311 | 1.9909 | 0.1235 | 0.1126 | 0.1178 | 0.5487 |
|
65 |
+
| 0.4948 | 10.0 | 4790 | 2.0021 | 0.1263 | 0.1171 | 0.1215 | 0.5544 |
|
66 |
+
|
67 |
+
|
68 |
+
### Framework versions
|
69 |
+
|
70 |
+
- Transformers 4.30.2
|
71 |
+
- Pytorch 1.12.1
|
72 |
+
- Datasets 2.13.2
|
73 |
+
- Tokenizers 0.13.3
|