---
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