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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_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. -->
# sbert_large_nlu_ru_pos
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: 0.4870
- Precision: 0.5717
- Recall: 0.605
- F1: 0.5879
- Accuracy: 0.9001
## 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 | 1.09 | 50 | 0.6457 | 0.0 | 0.0 | 0.0 | 0.7571 |
| No log | 2.17 | 100 | 0.5343 | 0.0458 | 0.0463 | 0.0461 | 0.7998 |
| No log | 3.26 | 150 | 0.3732 | 0.1121 | 0.1486 | 0.1278 | 0.8512 |
| No log | 4.35 | 200 | 0.3237 | 0.2713 | 0.3436 | 0.3032 | 0.8778 |
| No log | 5.43 | 250 | 0.2921 | 0.3412 | 0.4189 | 0.3761 | 0.8935 |
| No log | 6.52 | 300 | 0.2778 | 0.4079 | 0.5386 | 0.4642 | 0.9011 |
| No log | 7.61 | 350 | 0.2989 | 0.4301 | 0.4807 | 0.4540 | 0.9012 |
| No log | 8.7 | 400 | 0.2617 | 0.4489 | 0.5676 | 0.5013 | 0.9083 |
| No log | 9.78 | 450 | 0.3645 | 0.4661 | 0.5174 | 0.4904 | 0.9050 |
| 0.3288 | 10.87 | 500 | 0.3305 | 0.5297 | 0.6023 | 0.5637 | 0.9126 |
| 0.3288 | 11.96 | 550 | 0.3256 | 0.5544 | 0.6004 | 0.5765 | 0.9093 |
| 0.3288 | 13.04 | 600 | 0.3275 | 0.4330 | 0.5927 | 0.5004 | 0.9093 |
| 0.3288 | 14.13 | 650 | 0.4194 | 0.5017 | 0.5618 | 0.5301 | 0.9123 |
| 0.3288 | 15.22 | 700 | 0.3667 | 0.5275 | 0.6100 | 0.5658 | 0.9138 |
| 0.3288 | 16.3 | 750 | 0.4694 | 0.5117 | 0.6351 | 0.5668 | 0.9087 |
| 0.3288 | 17.39 | 800 | 0.4007 | 0.5381 | 0.6139 | 0.5735 | 0.9098 |
| 0.3288 | 18.48 | 850 | 0.3834 | 0.5264 | 0.5965 | 0.5593 | 0.9103 |
| 0.3288 | 19.57 | 900 | 0.4039 | 0.5061 | 0.6371 | 0.5641 | 0.9078 |
| 0.3288 | 20.65 | 950 | 0.5111 | 0.5850 | 0.6042 | 0.5945 | 0.9107 |
| 0.0507 | 21.74 | 1000 | 0.5454 | 0.5699 | 0.5985 | 0.5838 | 0.9124 |
| 0.0507 | 22.83 | 1050 | 0.4575 | 0.5668 | 0.6139 | 0.5894 | 0.9148 |
| 0.0507 | 23.91 | 1100 | 0.3752 | 0.5281 | 0.6178 | 0.5694 | 0.9126 |
| 0.0507 | 25.0 | 1150 | 0.5141 | 0.6074 | 0.6332 | 0.6200 | 0.9159 |
| 0.0507 | 26.09 | 1200 | 0.4203 | 0.5464 | 0.6371 | 0.5882 | 0.9134 |
| 0.0507 | 27.17 | 1250 | 0.4810 | 0.5150 | 0.6313 | 0.5672 | 0.9115 |
| 0.0507 | 28.26 | 1300 | 0.4972 | 0.5560 | 0.5753 | 0.5655 | 0.9116 |
| 0.0507 | 29.35 | 1350 | 0.6118 | 0.5439 | 0.6216 | 0.5802 | 0.9127 |
| 0.0507 | 30.43 | 1400 | 0.5298 | 0.4354 | 0.6371 | 0.5172 | 0.8847 |
| 0.0507 | 31.52 | 1450 | 0.5129 | 0.5771 | 0.6216 | 0.5985 | 0.9132 |
| 0.0234 | 32.61 | 1500 | 0.5165 | 0.5395 | 0.6332 | 0.5826 | 0.9068 |
| 0.0234 | 33.7 | 1550 | 0.4776 | 0.5110 | 0.6255 | 0.5625 | 0.9095 |
| 0.0234 | 34.78 | 1600 | 0.3794 | 0.5156 | 0.6699 | 0.5827 | 0.9117 |
| 0.0234 | 35.87 | 1650 | 0.4895 | 0.6074 | 0.6332 | 0.6200 | 0.9165 |
| 0.0234 | 36.96 | 1700 | 0.5130 | 0.6317 | 0.6158 | 0.6237 | 0.9137 |
| 0.0234 | 38.04 | 1750 | 0.5138 | 0.6143 | 0.6120 | 0.6132 | 0.9103 |
| 0.0234 | 39.13 | 1800 | 0.5555 | 0.5579 | 0.6602 | 0.6048 | 0.9044 |
| 0.0234 | 40.22 | 1850 | 0.3895 | 0.5055 | 0.6197 | 0.5568 | 0.9107 |
| 0.0234 | 41.3 | 1900 | 0.4607 | 0.5936 | 0.6429 | 0.6172 | 0.9101 |
| 0.0234 | 42.39 | 1950 | 0.3913 | 0.5654 | 0.6429 | 0.6016 | 0.9091 |
| 0.0259 | 43.48 | 2000 | 0.3646 | 0.5797 | 0.6602 | 0.6173 | 0.9091 |
| 0.0259 | 44.57 | 2050 | 0.5094 | 0.6579 | 0.6274 | 0.6423 | 0.9191 |
| 0.0259 | 45.65 | 2100 | 0.4718 | 0.5996 | 0.6158 | 0.6076 | 0.9124 |
| 0.0259 | 46.74 | 2150 | 0.5557 | 0.5855 | 0.6409 | 0.6120 | 0.9056 |
| 0.0259 | 47.83 | 2200 | 0.5481 | 0.6018 | 0.6332 | 0.6171 | 0.9106 |
| 0.0259 | 48.91 | 2250 | 0.5198 | 0.5535 | 0.6486 | 0.5973 | 0.9104 |
| 0.0259 | 50.0 | 2300 | 0.4876 | 0.6282 | 0.6197 | 0.6239 | 0.9098 |
| 0.0259 | 51.09 | 2350 | 0.4904 | 0.5352 | 0.5135 | 0.5241 | 0.8984 |
| 0.0259 | 52.17 | 2400 | 0.4268 | 0.5639 | 0.6390 | 0.5991 | 0.9080 |
| 0.0259 | 53.26 | 2450 | 0.4759 | 0.5695 | 0.5772 | 0.5733 | 0.9057 |
| 0.0221 | 54.35 | 2500 | 0.5927 | 0.6129 | 0.5869 | 0.5996 | 0.9017 |
| 0.0221 | 55.43 | 2550 | 0.4404 | 0.4917 | 0.6274 | 0.5513 | 0.8964 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2
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