README.md

metadata

language:
  - tw
tags:
  - albert
  - classification
license: afl-3.0
metrics:
  - Accuracy

繁體中文情緒分類: 負面(0)、正面(1)

依據ckiplab/albert預訓練模型微調，訓練資料集只有8萬筆，做為課程的範例模型。

使用範例:

from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("clhuang/albert-sentiment")
model = AutoModelForSequenceClassification.from_pretrained("clhuang/albert-sentiment")

## Pediction
target_names=['Negative','Positive']
max_length = 200 # 最多字數 若超出模型訓練時的字數，以模型最大字數為依據 
def get_sentiment_proba(text):
    # prepare our text into tokenized sequence
    inputs = tokenizer(text, padding=True, truncation=True, max_length=max_length, return_tensors="pt")
    # perform inference to our model
    outputs = model(**inputs)
    # get output probabilities by doing softmax
    probs = outputs[0].softmax(1)

    response = {'Negative': round(float(probs[0, 0]), 2), 'Positive': round(float(probs[0, 1]), 2)}
    # executing argmax function to get the candidate label
    #return probs.argmax()
    return response

get_sentiment_proba('我喜歡這本書')
get_sentiment_proba('不喜歡這款產品')