File size: 5,562 Bytes
0a9d877 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 |
---
base_model: alexyalunin/RuBioRoBERTa
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: RuBioRoBERTa_pos
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# RuBioRoBERTa_pos
This model is a fine-tuned version of [alexyalunin/RuBioRoBERTa](https://huggingface.co/alexyalunin/RuBioRoBERTa) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5510
- Precision: 0.6388
- Recall: 0.5954
- F1: 0.6163
- Accuracy: 0.9111
## 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:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 50 | 0.6556 | 0.0 | 0.0 | 0.0 | 0.7611 |
| No log | 2.0 | 100 | 1.2213 | 0.0011 | 0.0019 | 0.0014 | 0.2513 |
| No log | 3.0 | 150 | 0.6117 | 0.0 | 0.0 | 0.0 | 0.7642 |
| No log | 4.0 | 200 | 0.5155 | 0.0135 | 0.0405 | 0.0203 | 0.7884 |
| No log | 5.0 | 250 | 0.4171 | 0.0697 | 0.1715 | 0.0991 | 0.8268 |
| No log | 6.0 | 300 | 0.3536 | 0.1054 | 0.1753 | 0.1317 | 0.8594 |
| No log | 7.0 | 350 | 0.3714 | 0.1638 | 0.2216 | 0.1884 | 0.8685 |
| No log | 8.0 | 400 | 0.2889 | 0.2477 | 0.3622 | 0.2942 | 0.8864 |
| No log | 9.0 | 450 | 0.2943 | 0.2799 | 0.3969 | 0.3283 | 0.8921 |
| 0.452 | 10.0 | 500 | 0.2916 | 0.3823 | 0.4817 | 0.4263 | 0.9011 |
| 0.452 | 11.0 | 550 | 0.3162 | 0.3329 | 0.4817 | 0.3937 | 0.8935 |
| 0.452 | 12.0 | 600 | 0.3245 | 0.3629 | 0.4971 | 0.4195 | 0.9040 |
| 0.452 | 13.0 | 650 | 0.3535 | 0.4022 | 0.4913 | 0.4423 | 0.9021 |
| 0.452 | 14.0 | 700 | 0.3313 | 0.4161 | 0.5588 | 0.4770 | 0.9023 |
| 0.452 | 15.0 | 750 | 0.3560 | 0.4210 | 0.5800 | 0.4878 | 0.9006 |
| 0.452 | 16.0 | 800 | 0.3980 | 0.4125 | 0.6224 | 0.4962 | 0.8905 |
| 0.452 | 17.0 | 850 | 0.3767 | 0.4820 | 0.6455 | 0.5519 | 0.9071 |
| 0.452 | 18.0 | 900 | 0.3947 | 0.4605 | 0.6513 | 0.5395 | 0.9034 |
| 0.452 | 19.0 | 950 | 0.4351 | 0.4395 | 0.5877 | 0.5029 | 0.9066 |
| 0.0844 | 20.0 | 1000 | 0.3581 | 0.4931 | 0.5530 | 0.5213 | 0.9097 |
| 0.0844 | 21.0 | 1050 | 0.4050 | 0.4892 | 0.6108 | 0.5433 | 0.9063 |
| 0.0844 | 22.0 | 1100 | 0.4893 | 0.5504 | 0.5472 | 0.5488 | 0.9076 |
| 0.0844 | 23.0 | 1150 | 0.4173 | 0.4722 | 0.6050 | 0.5304 | 0.9062 |
| 0.0844 | 24.0 | 1200 | 0.4307 | 0.4819 | 0.6146 | 0.5402 | 0.9075 |
| 0.0844 | 25.0 | 1250 | 0.3874 | 0.4977 | 0.6185 | 0.5515 | 0.9151 |
| 0.0844 | 26.0 | 1300 | 0.4591 | 0.5478 | 0.6513 | 0.5951 | 0.9130 |
| 0.0844 | 27.0 | 1350 | 0.3543 | 0.5308 | 0.5973 | 0.5621 | 0.9144 |
| 0.0844 | 28.0 | 1400 | 0.4676 | 0.5380 | 0.5453 | 0.5416 | 0.9187 |
| 0.0844 | 29.0 | 1450 | 0.4169 | 0.5365 | 0.6224 | 0.5763 | 0.9131 |
| 0.0401 | 30.0 | 1500 | 0.4394 | 0.5867 | 0.5607 | 0.5734 | 0.9114 |
| 0.0401 | 31.0 | 1550 | 0.4550 | 0.5446 | 0.6474 | 0.5915 | 0.9166 |
| 0.0401 | 32.0 | 1600 | 0.4592 | 0.5415 | 0.6166 | 0.5766 | 0.9125 |
| 0.0401 | 33.0 | 1650 | 0.5040 | 0.5218 | 0.6455 | 0.5771 | 0.9093 |
| 0.0401 | 34.0 | 1700 | 0.4609 | 0.4295 | 0.6686 | 0.5230 | 0.8989 |
| 0.0401 | 35.0 | 1750 | 0.6256 | 0.4833 | 0.6397 | 0.5506 | 0.8975 |
| 0.0401 | 36.0 | 1800 | 0.4697 | 0.5742 | 0.6185 | 0.5955 | 0.9088 |
| 0.0401 | 37.0 | 1850 | 0.5114 | 0.5645 | 0.6069 | 0.5850 | 0.9139 |
| 0.0401 | 38.0 | 1900 | 0.5884 | 0.6237 | 0.5780 | 0.6 | 0.9088 |
| 0.0401 | 39.0 | 1950 | 0.5022 | 0.5429 | 0.6455 | 0.5898 | 0.9135 |
| 0.0328 | 40.0 | 2000 | 0.4154 | 0.6315 | 0.6339 | 0.6327 | 0.9202 |
| 0.0328 | 41.0 | 2050 | 0.3940 | 0.5519 | 0.6146 | 0.5816 | 0.9145 |
| 0.0328 | 42.0 | 2100 | 0.3374 | 0.5477 | 0.6301 | 0.5860 | 0.9120 |
| 0.0328 | 43.0 | 2150 | 0.5907 | 0.5483 | 0.5029 | 0.5246 | 0.9041 |
| 0.0328 | 44.0 | 2200 | 0.4235 | 0.5606 | 0.6416 | 0.5984 | 0.9145 |
| 0.0328 | 45.0 | 2250 | 0.6646 | 0.0 | 0.0 | 0.0 | 0.7640 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2
|