vumichien/ner-jp-gliner
This model is a fine-tuned version of deberta-v3-base-small on the meals synthetic dataset that generated by Mistral 8B. It achieves the following results:
- Precision: 84.79%
- Recall: 75.04%
- F1 score: 79.62%
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:
- num_steps: 30000
- train_batch_size: 8
- eval_every: 3000
- warmup_ratio: 0.1
- scheduler_type: "cosine"
- loss_alpha: -1
- loss_gamma: 0
- label_smoothing: 0
- loss_reduction: "sum"
- lr_encoder: 1e-5
- lr_others: 5e-5
- weight_decay_encoder: 0.01
- weight_decay_other: 0.01
Training results
Epoch | Training Loss |
---|---|
1 | No log |
2 | 2008.786600 |
3 | 2008.786600 |
4 | 117.661100 |
5 | 84.863400 |
6 | 84.863400 |
7 | 66.872200 |
8 | 66.872200 |
9 | 58.574600 |
10 | 53.905900 |
11 | 53.905900 |
12 | 48.563900 |
13 | 48.563900 |
14 | 43.970700 |
15 | 38.940100 |
16 | 38.940100 |
17 | 35.543100 |
18 | 35.543100 |
19 | 33.050500 |
20 | 30.091100 |
21 | 30.091100 |
22 | 27.275200 |
23 | 27.275200 |
24 | 25.327500 |
25 | 23.171200 |
26 | 23.171200 |
27 | 20.940300 |
28 | 19.034100 |
29 | 19.034100 |
30 | 17.366400 |
31 | 17.366400 |
32 | 16.570800 |
33 | 15.673200 |
34 | 15.673200 |
35 | 14.457500 |
36 | 14.457500 |
37 | 13.064500 |
38 | 12.786100 |
39 | 12.786100 |
40 | 11.934400 |
41 | 11.934400 |
42 | 11.225800 |
43 | 10.106500 |
44 | 10.106500 |
45 | 9.200000 |
46 | 9.200000 |
47 | 9.449100 |
48 | 8.979400 |
49 | 8.979400 |
50 | 7.840100 |
51 | 7.949600 |
52 | 7.949600 |
53 | 7.233800 |
54 | 7.233800 |
55 | 7.383200 |
56 | 6.114800 |
57 | 6.114800 |
58 | 6.421800 |
59 | 6.421800 |
60 | 6.191000 |
61 | 5.932200 |
62 | 5.932200 |
63 | 5.706100 |
64 | 5.706100 |
65 | 5.567800 |
66 | 5.104100 |
67 | 5.104100 |
68 | 5.407800 |
69 | 5.407800 |
70 | 5.607500 |
71 | 4.967500 |
72 | 4.967500 |
73 | 5.362100 |
74 | 5.362100 |
75 | 5.425800 |
76 | 5.283100 |
77 | 5.283100 |
78 | 4.250000 |
79 | 4.330900 |
80 | 4.330900 |
81 | 4.088400 |
82 | 4.088400 |
83 | 4.512400 |
84 | 4.513500 |
85 | 4.513500 |
86 | 4.327000 |
87 | 4.327000 |
88 | 5.152200 |
89 | 3.776100 |
90 | 3.776100 |
91 | 3.762500 |
92 | 3.762500 |
93 | 4.054900 |
94 | 3.579700 |
95 | 3.579700 |
96 | 3.391500 |
97 | 3.391500 |
98 | 4.863200 |
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