Distilbert Base Uncased Sst2

🚀 用於情感分析的DistilBERT-Base-Uncased模型

本模型是 distilbert-base-uncased 的微調版本。該基礎模型最初發佈於論文 "DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter"，並在 斯坦福情感樹庫v2 (SST2) 上進行訓練，SST2是 通用語言理解評估 (GLUE) 基準測試的一部分。此模型由 AssemblyAI 團隊進行微調，並隨 相應博客文章 一同發佈。

🚀 快速開始

要下載並使用此模型進行情感分析，請執行以下操作：

💻 使用示例

基礎用法

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)+"%")

如果您對如何使用此模型有任何疑問，請隨時聯繫 AssemblyAI 團隊！