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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