Hugging Face's logo

gil923
/
bert-finetuned-ner

Token Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
Model card Files
xet
 Metrics Community
gil923 commited on
Commit
a339dca
·
verified ·
1 Parent(s): dbf0678

Training complete

Browse files
Files changed (1) hide show
  1. README.md +85 -0
README.md ADDED
@@ -0,0 +1,85 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: transformers
3
+ license: apache-2.0
4
+ base_model: bert-base-cased
5
+ tags:
6
+ - generated_from_trainer
7
+ metrics:
8
+ - precision
9
+ - recall
10
+ - f1
11
+ - accuracy
12
+ model-index:
13
+ - name: bert-finetuned-ner
14
+ results: []
15
+ ---
16
+
17
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
18
+ should probably proofread and complete it, then remove this comment. -->
19
+
20
+ # bert-finetuned-ner
21
+
22
+ This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
23
+ It achieves the following results on the evaluation set:
24
+ - Loss: 0.0616
25
+ - Precision: 0.9319
26
+ - Recall: 0.9488
27
+ - F1: 0.9403
28
+ - Accuracy: 0.9856
29
+
30
+ ## Model description
31
+
32
+ More information needed
33
+
34
+ ## Intended uses & limitations
35
+
36
+ More information needed
37
+
38
+ ## Training and evaluation data
39
+
40
+ More information needed
41
+
42
+ ## Training procedure
43
+
44
+ ### Training hyperparameters
45
+
46
+ The following hyperparameters were used during training:
47
+ - learning_rate: 2e-05
48
+ - train_batch_size: 8
49
+ - eval_batch_size: 8
50
+ - seed: 42
51
+ - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
52
+ - lr_scheduler_type: linear
53
+ - num_epochs: 3
54
+
55
+ ### Training results
56
+
57
+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
58
+ |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
59
+ | No log | 0.1503 | 264 | 0.1354 | 0.7782 | 0.8544 | 0.8145 | 0.9625 |
60
+ | 0.2679 | 0.3007 | 528 | 0.0971 | 0.8526 | 0.9005 | 0.8759 | 0.9736 |
61
+ | 0.2679 | 0.4510 | 792 | 0.0887 | 0.8900 | 0.9222 | 0.9059 | 0.9781 |
62
+ | 0.105 | 0.6014 | 1056 | 0.0809 | 0.9094 | 0.9278 | 0.9185 | 0.9804 |
63
+ | 0.105 | 0.7517 | 1320 | 0.0714 | 0.9137 | 0.9342 | 0.9239 | 0.9812 |
64
+ | 0.0748 | 0.9021 | 1584 | 0.0645 | 0.9181 | 0.9377 | 0.9278 | 0.9836 |
65
+ | 0.0748 | 1.0524 | 1848 | 0.0735 | 0.9173 | 0.9392 | 0.9282 | 0.9825 |
66
+ | 0.0634 | 1.2027 | 2112 | 0.0692 | 0.9129 | 0.9389 | 0.9257 | 0.9826 |
67
+ | 0.0634 | 1.3531 | 2376 | 0.0691 | 0.9297 | 0.9478 | 0.9387 | 0.9851 |
68
+ | 0.0428 | 1.5034 | 2640 | 0.0660 | 0.9229 | 0.9448 | 0.9337 | 0.9844 |
69
+ | 0.0428 | 1.6538 | 2904 | 0.0602 | 0.9292 | 0.9450 | 0.9370 | 0.9855 |
70
+ | 0.0448 | 1.8041 | 3168 | 0.0603 | 0.9165 | 0.9461 | 0.9311 | 0.9844 |
71
+ | 0.0448 | 1.9544 | 3432 | 0.0636 | 0.9311 | 0.9458 | 0.9384 | 0.9848 |
72
+ | 0.0364 | 2.1048 | 3696 | 0.0686 | 0.9305 | 0.9461 | 0.9383 | 0.9853 |
73
+ | 0.0364 | 2.2551 | 3960 | 0.0632 | 0.9338 | 0.9497 | 0.9417 | 0.9857 |
74
+ | 0.0211 | 2.4055 | 4224 | 0.0644 | 0.9284 | 0.9450 | 0.9366 | 0.9845 |
75
+ | 0.0211 | 2.5558 | 4488 | 0.0628 | 0.9331 | 0.9458 | 0.9394 | 0.9846 |
76
+ | 0.0209 | 2.7062 | 4752 | 0.0602 | 0.9287 | 0.9473 | 0.9379 | 0.9853 |
77
+ | 0.0221 | 2.8565 | 5016 | 0.0616 | 0.9319 | 0.9488 | 0.9403 | 0.9856 |
78
+
79
+
80
+ ### Framework versions
81
+
82
+ - Transformers 4.57.1
83
+ - Pytorch 2.8.0+cu126
84
+ - Datasets 4.0.0
85
+ - Tokenizers 0.22.1