---
license: cc-by-nc-4.0
datasets:
- vumichien/meals-data-gliner
language:
- en
library_name: gliner
---
# vumichien/ner-jp-gliner

This model is a fine-tuned version of [deberta-v3-base-small](microsoft/deberta-v3-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      |