roberta-lora-text-emotion-analysis

下面是模型的使用方式。

from datasets import load_dataset, DatasetDict, Dataset

from transformers import (
    AutoTokenizer,
    AutoConfig, 
    AutoModelForSequenceClassification,
    DataCollatorWithPadding,
    TrainingArguments,
    Trainer)
from pprint import pprint
from peft import get_peft_model, LoraConfig, PeftModel
import evaluate
import torch
import numpy as np
import pandas as pd
from transformers import AutoTokenizer, AutoModelForMaskedLM


base_model = "FacebookAI/roberta-large"
model_checkpoint = "pretrained_q_k_v"
# define label maps
id2label = {0: "Negative", 1: "Positive"}
label2id = {"Negative":0, "Positive":1}

inference_model = AutoModelForSequenceClassification.from_pretrained(
    base_model, num_labels=2, id2label=id2label, label2id=label2id
)
tokenizer = AutoTokenizer.from_pretrained(base_model)
model = PeftModel.from_pretrained(inference_model, model_checkpoint)
text = "I like it."
# 对输入文本进行编码
inputs = tokenizer(text, return_tensors="pt")

# 模型推理
with torch.no_grad():
    outputs = model(**inputs)

# 获取预测结果
predictions = torch.argmax(outputs.logits, dim=-1)

# 打印预测结果
print(outputs)
print(f"Predicted label: {id2label[predictions.item()]}")