import json import requests API_URL = "https://api-inference.huggingface.co/models/daigo/bert-base-japanese-sentiment" headers = {"Authorization": f"Bearer {API_TOKEN}"} def query(payload): data = json.dumps(payload) response = requests.request("POST", API_URL, headers=headers, data=data) return json.loads(response.content.decode("utf-8")) data = query({"inputs": "I like you. I love you"})
from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("daigo/bert-base-japanese-sentiment") model = AutoModelForSequenceClassification.from_pretrained("daigo/bert-base-japanese-sentiment")
git lfs install git clone https://huggingface.co/daigo/bert-base-japanese-sentiment
# if you want to clone without large files – just their pointers # prepend your git clone with the following env var: GIT_LFS_SKIP_SMUDGE=1
binary classification
print(pipeline("sentiment-analysis",model="daigo/bert-base-japanese-sentiment",tokenizer="daigo/bert-base-japanese-sentiment")("私は幸福である。")) [{'label': 'ポジティブ', 'score': 0.98430425}]
