利用微调的deberta-v3-large来预测情感分类
通过手动输入一段话来测试我们情感分类的大模型准确率,并且介绍了如何获取对应的预测下标和使用softmax函数来计算出相应的概率
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前言:
昨天我们讲述了怎么利用emotion数据集进行deberta-v3-large大模型的微调,那今天我们就来输入一些数据来测试一下,看看模型的准确率,为了方便起见,我直接用测试集的前十条数据
代码:
from transformers import AutoModelForSequenceClassification,AutoTokenizer
import torch
import numpy
tokenizer = AutoTokenizer.from_pretrained("deberta-v3-large")
model = AutoModelForSequenceClassification.from_pretrained("result/checkpoint-500",num_labels=6)
raw_inputs = [
"im feeling rather rotten so im not very ambitious right now",
"im updating my blog because i feel shitty",
"i never make her separate from me because i don t ever want her to feel like i m ashamed with her",
"i left with my bouquet of red and yellow tulips under my arm feeling slightly more optimistic than when i arrived",
"i was feeling a little vain when i did this one",
"i cant walk into a shop anywhere where i do not feel uncomfortable",
"i felt anger when at the end of a telephone call",
"i explain why i clung to a relationship with a boy who was in many ways immature and uncommitted despite the excitement i should have been feeling for g
etting accepted into the masters program at the university of virginia",
"i like to have the same breathless feeling as a reader eager to see what will happen next",
"i jest i feel grumpy tired and pre menstrual which i probably am but then again its only been a week and im about as fit as a walrus on vacation for the
summer"
]
inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors="pt")
outputs = model(**inputs)
print(outputs.logits.argmax(-1).numpy())
output_tensor = torch.softmax(outputs.logits, dim=1)
numpy.set_printoptions(suppress=True, precision=15)
print(output_tensor.detach().numpy())
标注结果:
[0 0 0 1 0 4 3 1 1 3]
测试结果:
[0 0 0 1 0 4 4 2 1 3]
[[0.99185866 0.0011510316 0.00038844926 0.0026896652 0.0029623401
0.00094986777]
[0.9918577 0.0011512033 0.00038886679 0.0026923663 0.0029585315
0.000951257 ]
[0.99185807 0.0011446937 0.00038163515 0.0026456509 0.0030354485
0.00093440723]
[0.00041773843 0.9972398 0.0014854104 0.0002909223 0.00036231524
0.00020376328]
[0.99185014 0.0011451623 0.00038086114 0.0026396883 0.0030524035
0.00093187904]
[0.015044774 0.0025362356 0.00041989447 0.015223678 0.95009714
0.016678285 ]
[0.11319714 0.030935207 0.007336047 0.3035547 0.47545433
0.069522515 ]
[0.0011094044 0.18334262 0.8081213 0.0011003793 0.0007297965
0.005596481 ]
[0.0004444314 0.9972433 0.0014491597 0.00028465112 0.00037411976
0.00020446534]
[0.00241266 0.00079152075 0.00092184055 0.9924028 0.0024109248
0.0010602956 ]]
结果对比:
除了第七、第八条数据错误外,其他的八条数据都是正确的
代码解释:
1、raw_inputs:用户输入的数据,这个地方你可以使用一个while循环,然后使用input来与用户进行交互,需要注意的是这个必须是一个数组,哪怕用户只输入了一句文本。
2、return_tensors="pt":表示tokenizer返回的是PyTorch格式的数据
3、argmax(-1):将logits属性中的浮点数张量沿着最后一个轴(即-1轴)进行argmax操作,从而找到该张量中最大值所对应的标签编号。
4、softmax(outputs.logits, dim=1):dim指沿着哪个维度计算softmax,通常指定为1,表示对每一行进行softmax操作。如果不指定,则默认在最后一维计算softmax。
5、numpy.set_printoptions(suppress=True, precision=15):使用 numpy.set_printoptions()
函数来设置打印选项,从而调整打印输出格式。其中,suppress
选项可以关闭科学计数法,precision
选项可以设置打印精度。
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