wb-themes-classification

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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

Framework versions

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