# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("kkatodus/jp-speech-classifier")
model = AutoModelForSequenceClassification.from_pretrained("kkatodus/jp-speech-classifier")Quick Links
jp-speech-classifier
This model is a fine-tuned version of cl-tohoku/bert-base-japanese-v3 on a dataset created from speech records in the Japanese diet. It achieves the following results on the evaluation set:
- Loss: 1.1895
- Accuracy: 0.7053
Model description
This model classifies Japanese sentences into factual, question, descriptive, opinion based and other sentences.
Intended uses & limitations
This model can be used for any purpose that requires sentence categorization of Japanese sentences. The dataset is fairly small but it gets the job done.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 72 | 1.1048 | 0.6772 |
| No log | 2.0 | 144 | 1.1895 | 0.7053 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
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Model tree for kkatodus/jp-speech-classifier
Base model
tohoku-nlp/bert-base-japanese-v3
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kkatodus/jp-speech-classifier")