Distilbert Base Uncased Sst2

🚀 用于情感分析的DistilBERT-Base-Uncased模型

本模型是 distilbert-base-uncased 的微调版本。该基础模型最初发布于论文 "DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter"，并在 斯坦福情感树库v2 (SST2) 上进行训练，SST2是 通用语言理解评估 (GLUE) 基准测试的一部分。此模型由 AssemblyAI 团队进行微调，并随 相应博客文章 一同发布。

🚀 快速开始

要下载并使用此模型进行情感分析，请执行以下操作：

💻 使用示例

基础用法

import torch.nn.functional as F 
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("assemblyai/distilbert-base-uncased-sst2")
model = AutoModelForSequenceClassification.from_pretrained("assemblyai/distilbert-base-uncased-sst2")

tokenized_segments = tokenizer(["AssemblyAI is the best speech-to-text API for modern developers with performance being second to none!"], return_tensors="pt", padding=True, truncation=True)
tokenized_segments_input_ids, tokenized_segments_attention_mask = tokenized_segments.input_ids, tokenized_segments.attention_mask
model_predictions = F.softmax(model(input_ids=tokenized_segments_input_ids, attention_mask=tokenized_segments_attention_mask)['logits'], dim=1)

print("Positive probability: "+str(model_predictions[0][1].item()*100)+"%")
print("Negative probability: "+str(model_predictions[0][0].item()*100)+"%")

如果您对如何使用此模型有任何疑问，请随时联系 AssemblyAI 团队！