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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