README.md

---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
base_model: cointegrated/rubert-tiny
model-index:
- name: test_trainer
  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. -->

# test_trainer

This model is a fine-tuned version of [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7461
- Accuracy: 0.8310

## How to use:
```python
# themes = ['баги', 'открытие', 'баланс', 'рейтинг', 'ревизия', 'другое']

from transformers import AutoTokenizer, AutoModel
import torch
model_name = 'wyluilipe/wb-themes-classification'

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = BertForSequenceClassification.from_pretrained(model_name, num_labels=i+1)

text = "программа не работает"
encoded_input = tokenizer(text, return_tensors='pt')

with torch.no_grad():
   output = model(**encoded_input)
   probabilities = torch.nn.functional.softmax(output.logits, dim=-1)
   predicted_class = torch.argmax(probabilities).item()
```

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 60   | 0.7383          | 0.8404   |
| No log        | 2.0   | 120  | 0.8743          | 0.7840   |
| No log        | 3.0   | 180  | 0.7312          | 0.8169   |
| No log        | 4.0   | 240  | 0.6733          | 0.8404   |
| No log        | 5.0   | 300  | 0.7612          | 0.7981   |
| No log        | 6.0   | 360  | 0.7671          | 0.8122   |
| No log        | 7.0   | 420  | 0.7306          | 0.8263   |
| No log        | 8.0   | 480  | 0.7523          | 0.8263   |
| 0.1118        | 9.0   | 540  | 0.7645          | 0.8263   |
| 0.1118        | 10.0  | 600  | 0.7461          | 0.8310   |


### Framework versions

- Transformers 4.37.1
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.1