Hugging Face's logo

terzimert
/
bert-finetuned-ner-v4.009

Token Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Metrics Community
bert-finetuned-ner-v4.009
File size: 2,219 Bytes 
d9edbeb
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
 
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- caner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-v4.009
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: caner
      type: caner
      config: default
      split: train[56%:57%]
      args: default
    metrics:
    - name: Precision
      type: precision
      value: 0.9051008303677343
    - name: Recall
      type: recall
      value: 0.8430939226519337
    - name: F1
      type: f1
      value: 0.8729977116704805
    - name: Accuracy
      type: accuracy
      value: 0.9033665835411472
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# bert-finetuned-ner-v4.009

This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the caner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8267
- Precision: 0.9051
- Recall: 0.8431
- F1: 0.8730
- Accuracy: 0.9034

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2346        | 1.0   | 3228 | 0.6709          | 0.8530    | 0.8144 | 0.8332 | 0.8820   |
| 0.1486        | 2.0   | 6456 | 0.6723          | 0.9031    | 0.8243 | 0.8619 | 0.8975   |
| 0.0897        | 3.0   | 9684 | 0.8267          | 0.9051    | 0.8431 | 0.8730 | 0.9034   |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2