End of training
Browse files
README.md
CHANGED
@@ -1,6 +1,6 @@
|
|
1 |
---
|
2 |
license: apache-2.0
|
3 |
-
base_model:
|
4 |
tags:
|
5 |
- generated_from_trainer
|
6 |
datasets:
|
@@ -25,16 +25,16 @@ model-index:
|
|
25 |
metrics:
|
26 |
- name: Precision
|
27 |
type: precision
|
28 |
-
value: 0.
|
29 |
- name: Recall
|
30 |
type: recall
|
31 |
-
value: 0.
|
32 |
- name: F1
|
33 |
type: f1
|
34 |
-
value: 0.
|
35 |
- name: Accuracy
|
36 |
type: accuracy
|
37 |
-
value: 0.
|
38 |
---
|
39 |
|
40 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
@@ -42,13 +42,13 @@ should probably proofread and complete it, then remove this comment. -->
|
|
42 |
|
43 |
# Bert-NER
|
44 |
|
45 |
-
This model is a fine-tuned version of [
|
46 |
It achieves the following results on the evaluation set:
|
47 |
-
- Loss: 0.
|
48 |
-
- Precision: 0.
|
49 |
-
- Recall: 0.
|
50 |
-
- F1: 0.
|
51 |
-
- Accuracy: 0.
|
52 |
|
53 |
## Model description
|
54 |
|
@@ -68,32 +68,20 @@ More information needed
|
|
68 |
|
69 |
The following hyperparameters were used during training:
|
70 |
- learning_rate: 5e-05
|
71 |
-
- train_batch_size:
|
72 |
-
- eval_batch_size:
|
73 |
- seed: 42
|
74 |
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
75 |
- lr_scheduler_type: linear
|
76 |
-
- num_epochs:
|
77 |
|
78 |
### Training results
|
79 |
|
80 |
-
| Training Loss | Epoch | Step
|
81 |
-
|
82 |
-
|
|
83 |
-
| 0.
|
84 |
-
| 0.
|
85 |
-
| 0.0508 | 4.0 | 3500 | 0.0739 | 0.9619 | 0.9311 | 0.9463 | 0.9668 |
|
86 |
-
| 0.0473 | 5.0 | 4375 | 0.0686 | 0.9604 | 0.9382 | 0.9492 | 0.9685 |
|
87 |
-
| 0.0427 | 6.0 | 5250 | 0.0541 | 0.9716 | 0.9610 | 0.9663 | 0.9790 |
|
88 |
-
| 0.033 | 7.0 | 6125 | 0.0357 | 0.9934 | 0.9677 | 0.9804 | 0.9880 |
|
89 |
-
| 0.0223 | 8.0 | 7000 | 0.0236 | 0.9912 | 0.9815 | 0.9863 | 0.9915 |
|
90 |
-
| 0.0151 | 9.0 | 7875 | 0.0167 | 0.9899 | 0.9905 | 0.9902 | 0.9938 |
|
91 |
-
| 0.0107 | 10.0 | 8750 | 0.0096 | 0.9955 | 0.9919 | 0.9937 | 0.9960 |
|
92 |
-
| 0.0074 | 11.0 | 9625 | 0.0063 | 0.9961 | 0.9970 | 0.9965 | 0.9978 |
|
93 |
-
| 0.0051 | 12.0 | 10500 | 0.0042 | 0.9979 | 0.9974 | 0.9977 | 0.9985 |
|
94 |
-
| 0.0037 | 13.0 | 11375 | 0.0024 | 0.9988 | 0.9985 | 0.9987 | 0.9992 |
|
95 |
-
| 0.0023 | 14.0 | 12250 | 0.0015 | 0.9991 | 0.9994 | 0.9992 | 0.9995 |
|
96 |
-
| 0.0014 | 15.0 | 13125 | 0.0010 | 0.9992 | 0.9998 | 0.9995 | 0.9997 |
|
97 |
|
98 |
|
99 |
### Framework versions
|
|
|
1 |
---
|
2 |
license: apache-2.0
|
3 |
+
base_model: distilbert-base-uncased
|
4 |
tags:
|
5 |
- generated_from_trainer
|
6 |
datasets:
|
|
|
25 |
metrics:
|
26 |
- name: Precision
|
27 |
type: precision
|
28 |
+
value: 0.9624574848236965
|
29 |
- name: Recall
|
30 |
type: recall
|
31 |
+
value: 0.9300632384756199
|
32 |
- name: F1
|
33 |
type: f1
|
34 |
+
value: 0.9459831157565114
|
35 |
- name: Accuracy
|
36 |
type: accuracy
|
37 |
+
value: 0.9665913020348451
|
38 |
---
|
39 |
|
40 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
|
|
42 |
|
43 |
# Bert-NER
|
44 |
|
45 |
+
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ner dataset.
|
46 |
It achieves the following results on the evaluation set:
|
47 |
+
- Loss: 0.0764
|
48 |
+
- Precision: 0.9625
|
49 |
+
- Recall: 0.9301
|
50 |
+
- F1: 0.9460
|
51 |
+
- Accuracy: 0.9666
|
52 |
|
53 |
## Model description
|
54 |
|
|
|
68 |
|
69 |
The following hyperparameters were used during training:
|
70 |
- learning_rate: 5e-05
|
71 |
+
- train_batch_size: 32
|
72 |
+
- eval_batch_size: 32
|
73 |
- seed: 42
|
74 |
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
75 |
- lr_scheduler_type: linear
|
76 |
+
- num_epochs: 3
|
77 |
|
78 |
### Training results
|
79 |
|
80 |
+
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|
81 |
+
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
|
82 |
+
| No log | 1.0 | 438 | 0.0869 | 0.9598 | 0.9263 | 0.9427 | 0.9648 |
|
83 |
+
| 0.0834 | 2.0 | 876 | 0.0815 | 0.9627 | 0.9280 | 0.9450 | 0.9661 |
|
84 |
+
| 0.054 | 3.0 | 1314 | 0.0764 | 0.9625 | 0.9301 | 0.9460 | 0.9666 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
85 |
|
86 |
|
87 |
### Framework versions
|