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---
tags:
- generated_from_trainer
language: ja
widget:
- text: 🤗セグメント利益は、前期比8.3％増の24億28百万円となった
metrics:
- accuracy
- f1
model-index:
- name: Japanese-sentiment-analysis
results: []
datasets:
- jarvisx17/chABSA
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# japanese-sentiment-analysis
This model is the work of jarvisx17 and 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 |