metadata
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
base_model: cointegrated/rubert-tiny
model-index:
- name: test_trainer
results: []
test_trainer
This model is a fine-tuned version of 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:
# 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