bert-japanese_finetuned-sentiment-analysis

This model was trained from scratch on the Japanese Sentiment Polarity Dictionary dataset.

Pre-trained model

jarvisx17/japanese-sentiment-analysis
Link : https://huggingface.co/jarvisx17/japanese-sentiment-analysis

Training Data

The model was trained on Japanese Sentiment Polarity Dictionary dataset.
link : https://www.cl.ecei.tohoku.ac.jp/Open_Resources-Japanese_Sentiment_Polarity_Dictionary.html

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("minutillamolinara/bert-japanese_finetuned-sentiment-analysis")

model = AutoModelForSequenceClassification.from_pretrained("minutillamolinara/bert-japanese_finetuned-sentiment-analysis")

inputs = tokenizer("自然言語処理が面白い", return_tensors="pt")

outputs = model(**inputs)

Dependencies

• !pip install fugashi
• !pip install unidic_lite

Licenses

MIT