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---
base_model: ai-forever/sbert_large_nlu_ru
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: sbert_large_nlu_ru_neg
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. -->
# sbert_large_nlu_ru_neg
This model is a fine-tuned version of [ai-forever/sbert_large_nlu_ru](https://huggingface.co/ai-forever/sbert_large_nlu_ru) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7106
- Precision: 0.5205
- Recall: 0.57
- F1: 0.5442
- Accuracy: 0.8956
## 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 | 1.0870 | 50 | 0.6440 | 0.0 | 0.0 | 0.0 | 0.7571 |
| No log | 2.1739 | 100 | 0.5237 | 0.0317 | 0.0579 | 0.0410 | 0.8069 |
| No log | 3.2609 | 150 | 0.3775 | 0.1163 | 0.1544 | 0.1327 | 0.8514 |
| No log | 4.3478 | 200 | 0.3368 | 0.2292 | 0.3031 | 0.2610 | 0.8769 |
| No log | 5.4348 | 250 | 0.3055 | 0.3066 | 0.3475 | 0.3258 | 0.8929 |
| No log | 6.5217 | 300 | 0.2919 | 0.3814 | 0.5463 | 0.4492 | 0.8989 |
| No log | 7.6087 | 350 | 0.2798 | 0.4372 | 0.5039 | 0.4682 | 0.9055 |
| No log | 8.6957 | 400 | 0.2730 | 0.3934 | 0.5560 | 0.4608 | 0.9071 |
| No log | 9.7826 | 450 | 0.3021 | 0.4666 | 0.5656 | 0.5113 | 0.9101 |
| 0.3321 | 10.8696 | 500 | 0.3249 | 0.4664 | 0.6023 | 0.5257 | 0.9110 |
| 0.3321 | 11.9565 | 550 | 0.3317 | 0.5316 | 0.5849 | 0.5570 | 0.9113 |
| 0.3321 | 13.0435 | 600 | 0.3352 | 0.4984 | 0.5946 | 0.5423 | 0.9127 |
| 0.3321 | 14.1304 | 650 | 0.3651 | 0.5079 | 0.5579 | 0.5317 | 0.9157 |
| 0.3321 | 15.2174 | 700 | 0.3856 | 0.4670 | 0.6004 | 0.5253 | 0.9083 |
| 0.3321 | 16.3043 | 750 | 0.4087 | 0.4905 | 0.5985 | 0.5391 | 0.9139 |
| 0.3321 | 17.3913 | 800 | 0.4108 | 0.5058 | 0.5869 | 0.5433 | 0.9113 |
| 0.3321 | 18.4783 | 850 | 0.3900 | 0.5597 | 0.6429 | 0.5984 | 0.9172 |
| 0.3321 | 19.5652 | 900 | 0.4572 | 0.5567 | 0.6158 | 0.5848 | 0.9168 |
| 0.3321 | 20.6522 | 950 | 0.4945 | 0.5952 | 0.5734 | 0.5841 | 0.9121 |
| 0.0516 | 21.7391 | 1000 | 0.5660 | 0.5835 | 0.5463 | 0.5643 | 0.9066 |
| 0.0516 | 22.8261 | 1050 | 0.4464 | 0.5307 | 0.6178 | 0.5709 | 0.9160 |
| 0.0516 | 23.9130 | 1100 | 0.5044 | 0.5696 | 0.6081 | 0.5882 | 0.9130 |
| 0.0516 | 25.0 | 1150 | 0.4807 | 0.5682 | 0.6274 | 0.5963 | 0.9151 |
| 0.0516 | 26.0870 | 1200 | 0.5006 | 0.5615 | 0.6525 | 0.6036 | 0.9157 |
| 0.0516 | 27.1739 | 1250 | 0.5228 | 0.6008 | 0.5985 | 0.5996 | 0.9127 |
| 0.0516 | 28.2609 | 1300 | 0.5091 | 0.5193 | 0.5965 | 0.5553 | 0.9117 |
| 0.0516 | 29.3478 | 1350 | 0.5135 | 0.6036 | 0.6409 | 0.6217 | 0.9177 |
| 0.0516 | 30.4348 | 1400 | 0.5183 | 0.5742 | 0.6351 | 0.6031 | 0.9157 |
| 0.0516 | 31.5217 | 1450 | 0.5202 | 0.5722 | 0.6506 | 0.6089 | 0.9106 |
| 0.0256 | 32.6087 | 1500 | 0.5170 | 0.5836 | 0.6602 | 0.6196 | 0.9174 |
| 0.0256 | 33.6957 | 1550 | 0.4348 | 0.6067 | 0.6313 | 0.6187 | 0.9215 |
| 0.0256 | 34.7826 | 1600 | 0.5070 | 0.6143 | 0.6120 | 0.6132 | 0.9156 |
| 0.0256 | 35.8696 | 1650 | 0.5840 | 0.6525 | 0.5907 | 0.6201 | 0.9121 |
| 0.0256 | 36.9565 | 1700 | 0.5587 | 0.5941 | 0.6274 | 0.6103 | 0.9124 |
| 0.0256 | 38.0435 | 1750 | 0.4073 | 0.5159 | 0.6564 | 0.5777 | 0.9117 |
| 0.0256 | 39.1304 | 1800 | 0.4428 | 0.6180 | 0.6371 | 0.6274 | 0.9166 |
| 0.0256 | 40.2174 | 1850 | 0.4775 | 0.5797 | 0.6390 | 0.6079 | 0.9199 |
| 0.0256 | 41.3043 | 1900 | 0.4121 | 0.5920 | 0.6274 | 0.6092 | 0.9171 |
| 0.0256 | 42.3913 | 1950 | 0.4683 | 0.6136 | 0.6467 | 0.6297 | 0.9179 |
| 0.0231 | 43.4783 | 2000 | 0.4961 | 0.6390 | 0.5946 | 0.6160 | 0.9137 |
| 0.0231 | 44.5652 | 2050 | 0.6040 | 0.6242 | 0.5483 | 0.5838 | 0.9031 |
| 0.0231 | 45.6522 | 2100 | 0.5498 | 0.6458 | 0.5985 | 0.6212 | 0.9121 |
| 0.0231 | 46.7391 | 2150 | 0.4636 | 0.6049 | 0.6236 | 0.6141 | 0.9212 |
| 0.0231 | 47.8261 | 2200 | 0.4797 | 0.634 | 0.6120 | 0.6228 | 0.9142 |
| 0.0231 | 48.9130 | 2250 | 0.5335 | 0.5134 | 0.6680 | 0.5805 | 0.9061 |
| 0.0231 | 50.0 | 2300 | 0.5348 | 0.6167 | 0.6120 | 0.6143 | 0.9075 |
| 0.0231 | 51.0870 | 2350 | 0.4871 | 0.6144 | 0.6429 | 0.6283 | 0.9085 |
| 0.0231 | 52.1739 | 2400 | 0.4767 | 0.5335 | 0.6757 | 0.5963 | 0.9082 |
| 0.0231 | 53.2609 | 2450 | 0.4494 | 0.5895 | 0.6486 | 0.6176 | 0.9109 |
| 0.0225 | 54.3478 | 2500 | 0.5282 | 0.5310 | 0.6448 | 0.5824 | 0.9088 |
| 0.0225 | 55.4348 | 2550 | 0.4321 | 0.5714 | 0.6332 | 0.6007 | 0.9148 |
| 0.0225 | 56.5217 | 2600 | 0.4822 | 0.6179 | 0.6274 | 0.6226 | 0.9105 |
| 0.0225 | 57.6087 | 2650 | 0.4360 | 0.5578 | 0.6429 | 0.5973 | 0.9150 |
| 0.0225 | 58.6957 | 2700 | 0.5101 | 0.6215 | 0.5927 | 0.6067 | 0.9083 |
| 0.0225 | 59.7826 | 2750 | 0.4751 | 0.5327 | 0.6602 | 0.5897 | 0.9069 |
| 0.0225 | 60.8696 | 2800 | 0.4942 | 0.6471 | 0.5946 | 0.6197 | 0.9065 |
| 0.0225 | 61.9565 | 2850 | 0.3628 | 0.4646 | 0.6332 | 0.5359 | 0.8957 |
| 0.0225 | 63.0435 | 2900 | 0.4447 | 0.6152 | 0.6236 | 0.6194 | 0.9098 |
| 0.0225 | 64.1304 | 2950 | 0.4965 | 0.5624 | 0.6525 | 0.6041 | 0.9130 |
| 0.0285 | 65.2174 | 3000 | 0.5616 | 0.5649 | 0.6216 | 0.5919 | 0.9082 |
| 0.0285 | 66.3043 | 3050 | 0.7228 | 0.65 | 0.5019 | 0.5664 | 0.8881 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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