japanese-sentiment-analysis
This model was trained from scratch on the chABSA dataset. It achieves the following results on the evaluation set:
- Loss: 0.0001
- Accuracy: 1.0
- F1: 1.0
Model description
Model Train for Japanese sentence sentiments.
Intended uses & limitations
The model was trained on chABSA Japanese dataset. DATASET link : https://www.kaggle.com/datasets/takahirokubo0/chabsa
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: 10
Usage
You can use cURL to access this model:
Python API:
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("jarvisx17/japanese-sentiment-analysis")
model = AutoModelForSequenceClassification.from_pretrained("jarvisx17/japanese-sentiment-analysis")
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
Training results
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
Dependencies
- !pip install fugashi
- !pip install unidic_lite