finetune content added

Author: AliNajafi1998

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+import{_ as d,o as l,c as r,p,e as m,a as t,f,b as e,w as n,d as g,g as k,F as b}from"./Cl6uyuXL.js";import{_ as y}from"./D5hmElPJ.js";import{_ as v}from"./VaQbr8R-.js";import"./tNQpnWyy.js";import"./sYAdBX6R.js";const T={},c=a=>(p("data-v-96987dbc"),a=a(),m(),a),w={class:"header-background flex flex-col items-center justify-center space-y-8 bg-cover bg-center bg-no-repeat py-10 md:py-16 lg:py-24 text-gray-200"},x=c(()=>t("h1",{class:"text-6xl"},"Hate Speech Detection",-1)),B=c(()=>t("hr",{class:"w-1/12"},null,-1)),E=c(()=>t("p",{class:"text-2xl"},null,-1)),R=[x,B,E];function z(a,s){return l(),r("header",w,R)}const S=d(T,[["render",z],["__scopeId","data-v-96987dbc"]]),H={class:"inner pt-10"},C=t("h3",null,"HateSpeech Detection with TurkishBERTweet",-1),$=t("p",null,"In this section, we will guide you through the process of using TurkishBERTweet for detecting hate speech. TurkishBERTweet is specifically designed to analyze Turkish social media content, making it a powerful tool for identifying harmful language, ensuring a safer online environment. ",-1),M=t("h3",null,"Setting Up the Environment for TurkishBERTweet",-1),V=t("br",null,null,-1),I=t("br",null,null,-1),N=t("h5",null,"1. Clone the Repository: Begin by cloning the TurkishBERTweet repository from GitHub.",-1),P=t("h5",null,"2. Navigate to the Directory: Move into the newly cloned directory.",-1),A=t("h5",null,"3. Set Up a Virtual Environment: Create a virtual environment to manage dependencies.",-1),L=t("h5",null,"4. Activate the Virtual Environment: Activate the virtual environment.",-1),F=t("h5",null,"5. Install Required Libraries: Install PyTorch and other essential libraries to run TurkishBERTweet.",-1),q=t("h5",null,"6. Below is a Python script that uses TurkishBERTweet to detect hate speech in sample texts:",-1),D=t("br",null,null,-1),U="git clone [email protected]:ViralLab/TurkishBERTweet.git",Y="cd TurkishBERTweet",j="python -m venv venv",G=`pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
+pip install peft
+pip install transformers
+pip install urlextract`,O=`import torch
+from peft import (
+    PeftModel,
+    PeftConfig,
+)
+
+from transformers import (
+    AutoModelForSequenceClassification,
+    AutoTokenizer)
+from Preprocessor import preprocess
+ 
+
+peft_model = "VRLLab/TurkishBERTweet-Lora-HS"
+peft_config = PeftConfig.from_pretrained(peft_model)
+
+# loading Tokenizer
+padding_side = "right"
+tokenizer = AutoTokenizer.from_pretrained(
+    peft_config.base_model_name_or_path, padding_side=padding_side
+)
+if getattr(tokenizer, "pad_token_id") is None:
+    tokenizer.pad_token_id = tokenizer.eos_token_id
+
+id2label_hs = {0: "No", 1: "Yes"}
+turkishBERTweet_hs = AutoModelForSequenceClassification.from_pretrained(
+    peft_config.base_model_name_or_path, return_dict=True, num_labels=len(id2label_hs), id2label=id2label_hs
+)
+turkishBERTweet_hs = PeftModel.from_pretrained(turkishBERTweet_hs, peft_model)
+
+
+sample_texts = [
+    "Viral lab da insanlar hep birlikte çalışıyorlar. hepbirlikte çalışan insanlar birbirlerine yakın oluyorlar.",     
+    "kasmayin artik ya kac kere tanik olduk bu azgin tehlikeli “multecilerin” yaptiklarina? bir afgan taragindan kafasi tasla ezilip tecavuz edilen kiza da git boyle cihangir solculugu yap yerse?",
+    ]
+
+
+preprocessed_texts = [preprocess(s) for s in sample_texts]
+with torch.no_grad():
+    for s in preprocessed_texts:
+        ids = tokenizer.encode_plus(s, return_tensors="pt")
+        label_id = turkishBERTweet_hs(**ids).logits.argmax(-1).item()
+        print(id2label_hs[label_id],":", s)
+`,W=`No : viral lab da insanlar hep birlikte çalışıyorlar. hepbirlikte çalışan insanlar birbirlerine yakın oluyorlar.
+Yes : kasmayin artik ya kac kere tanik olduk bu azgin tehlikeli “multecilerin” yaptiklarina? bir afgan taragindan kafasi tasla ezilip tecavuz edilen kiza da git boyle cihangir solculugu yap yerse?
+`,J="python run_hatespeech.py",K=f({__name:"HateSpeechMain",setup(a){const s=String.raw`#Linux/Mac users
+source venv/bin/activate
+# Windows users
+.\venv\Scripts\activate
+`;return(_,u)=>{const i=y,o=k,h=v;return l(),r("main",H,[e(h,null,{default:n(()=>[t("div",null,[C,$,M,g(" To begin using the TurkishBERTweet model for sentiment analysis, you'll first need to set up your development environment. This involves cloning the TurkishBERTweet repository, creating a virtual environment, and installing the necessary libraries. Follow these steps to get started: "),V,I,N,e(o,null,{default:n(()=>[e(i,{code:U,language:"bash",filename:"clone.sh"})]),_:1}),P,e(o,null,{default:n(()=>[e(i,{code:Y,language:"bash",filename:"cd.sh"})]),_:1}),A,e(o,null,{default:n(()=>[e(i,{code:j,language:"bash",filename:"venv.sh"})]),_:1}),L,e(o,null,{default:n(()=>[e(i,{code:s,language:"bash",filename:"activate.sh"})]),_:1}),F,e(o,null,{default:n(()=>[e(i,{code:G,language:"text",filename:"install.sh"})]),_:1}),q,e(o,null,{default:n(()=>[e(i,{code:J,language:"bash",filename:"run_finetune.sh"})]),_:1}),e(o,null,{default:n(()=>[e(i,{code:O,language:"python",filename:"run_hatespeech.py"})]),_:1}),e(o,null,{default:n(()=>[e(i,{code:W,filename:"output.txt"})]),_:1})]),D]),_:1})])}}}),Q={};function X(a,s){const _=S,u=K;return l(),r(b,null,[e(_),e(u)],64)}const oe=d(Q,[["render",X]]);export{oe as default};