Hugging Face's logo

malduwais
/
distilbert-base-uncased-finetuned-ner

Token Classification
Transformers
PyTorch
TensorBoard
Safetensors
distilbert
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
3
malduwais commited on
Commit
ab8cf90
•
1 Parent(s): 378a858

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +15 -15
README.md CHANGED
@@ -22,16 +22,16 @@ model-index:
22
  metrics:
23
  - name: Precision
24
  type: precision
25
- value: 0.9265618077093487
26
  - name: Recall
27
  type: recall
28
- value: 0.9357870007830854
29
  - name: F1
30
  type: f1
31
- value: 0.9311515556297656
32
  - name: Accuracy
33
  type: accuracy
34
- value: 0.9838117781625813
35
  ---
36
 
37
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -41,11 +41,11 @@ should probably proofread and complete it, then remove this comment. -->
41
 
42
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
43
  It achieves the following results on the evaluation set:
44
- - Loss: 0.0614
45
- - Precision: 0.9266
46
- - Recall: 0.9358
47
- - F1: 0.9312
48
- - Accuracy: 0.9838
49
 
50
  ## Model description
51
 
@@ -76,14 +76,14 @@ The following hyperparameters were used during training:
76
 
77
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
78
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
79
- | 0.2393 | 1.0 | 878 | 0.0719 | 0.9105 | 0.9207 | 0.9156 | 0.9806 |
80
- | 0.051 | 2.0 | 1756 | 0.0619 | 0.9203 | 0.9335 | 0.9269 | 0.9828 |
81
- | 0.0308 | 3.0 | 2634 | 0.0614 | 0.9266 | 0.9358 | 0.9312 | 0.9838 |
82
 
83
 
84
  ### Framework versions
85
 
86
- - Transformers 4.12.3
87
- - Pytorch 1.9.0+cu111
88
- - Datasets 1.15.1
89
  - Tokenizers 0.10.3
 
22
  metrics:
23
  - name: Precision
24
  type: precision
25
+ value: 0.9300908486594284
26
  - name: Recall
27
  type: recall
28
+ value: 0.9391430808815304
29
  - name: F1
30
  type: f1
31
+ value: 0.9345950459226274
32
  - name: Accuracy
33
  type: accuracy
34
+ value: 0.9842407104389407
35
  ---
36
 
37
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
41
 
42
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
43
  It achieves the following results on the evaluation set:
44
+ - Loss: 0.0589
45
+ - Precision: 0.9301
46
+ - Recall: 0.9391
47
+ - F1: 0.9346
48
+ - Accuracy: 0.9842
49
 
50
  ## Model description
51
 
 
76
 
77
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
78
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
79
+ | 0.2402 | 1.0 | 878 | 0.0692 | 0.9177 | 0.9248 | 0.9213 | 0.9815 |
80
+ | 0.0506 | 2.0 | 1756 | 0.0600 | 0.9249 | 0.9361 | 0.9305 | 0.9836 |
81
+ | 0.0304 | 3.0 | 2634 | 0.0589 | 0.9301 | 0.9391 | 0.9346 | 0.9842 |
82
 
83
 
84
  ### Framework versions
85
 
86
+ - Transformers 4.12.5
87
+ - Pytorch 1.10.0+cu111
88
+ - Datasets 1.16.1
89
  - Tokenizers 0.10.3