Edit model card


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


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


  • !pip install fugashi
  • !pip install unidic_lite
Downloads last month
Hosted inference API
Text Classification
This model can be loaded on the Inference API on-demand.

Space using jarvisx17/japanese-sentiment-analysis 1