silky smith

1630056297

Resolve Issues Of HP Printer With Print And Scan Doctor Download

Resolve issues Of HP Printer with print and scan doctor download and get the free utility tool for Windows in order to help resolve printing and scanning problems online. Our experts will provide you step by step guide to fix issues with printers in an easy way. Reach us now for free.

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Broris Holt

Broris Holt

1623333715

How to Fix the Error If "HP Printer is in Error State”?

“HP printer is in an error state” this error generally flashes on screen when HP Printer user tries to connect Printer to the device or print from the device. But this is not the only reason behind HP printer is in error state message or error code, It can also be caused by changes in software, such as Windows Update, that lead to an increase in communication between your system and the attached printer.

However, if you are looking for some quick method to resolve HP Printer in Error State then you should need to have a look at the below-explained tips: How do I get my HP Printer out of error state?

What do you do if Your HP Printer is in Error State?

Method 1: Run troubleshooter.

Method 2: Check Wi-Fi or Cable make sure any other cables are not connected to the printer as well.

Method 3: Check if there are any new updates on software.

Method 4: Make sure that your printer should be directly connected to the wall outlet and not to any surge protectors.

Method 5: If the printer is in Offline state, follow the below steps to make it Online.

  1. Click Windows + X, go to Control Panel and click on Devices and printers.
  2. Click Printer.
  3. If the Printer is offline, it shows “Offline” status. If the Printer is online it shows “Ready”.
  4. If the Printer is offline, set it to online.
  5. Right-click on the printer and select “Use Printer Online”.
  6. The display should change to ready when the printer is set to online.

Method 6: Download HP Print and Scan Doctor software to resolve the problem of the printer drivers

Method 7: If the problem still persists then reinstall the printer driver.

Method 8: Check Your Connection And Restart Your Devices:

Windows 10 user can also fix this issue in short time of span look here for detailed method to fix HP Printer Error State Windows 10.

#hp printer is in an error state #hp printer is in error state #printer in error state hp #printer is in an error state hp #hp printer in error state

Error Code

1618305449

Follow & Fix HP Printer Driver Install Error $ 1603 Effortlessly

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The HP is an international brand and used by millions of users around the world to get amazing prints and printers are not the only things that HP is popular for. However, the HP printers are simply awesome and work amazingly for everyone. The users of the HP printers can choose the printers to form the wide range of the printers the HP offer for the users, but sometimes while Install HP Printer Driver, the users come across a fatal error 1603.

About HP Printer Error 1603:

The HP Printer Install Error 1603 is the error that interrupts the users of the HP printers when they are installing software of the HP printers on the Windows OS and the Windows is trying to install more than one program at one time. The users have no option other than resolving the issue so that they can use the HP printer without any delay.

Here are the Easiest Steps To Fix HP Printer Driver Install Error 1603:

Given here are the quick steps in which you can resolve HP Error Code 1603 instantly.

  1.   The best way to eradicate this error code is to uninstall the driver. After a while, install it again to make it function well. Here are the steps to do so.
    
  2.   First of all, sign in the administrator mode, and then search for the Control panel.
    
  3.   When you open the control panel, you will look an option there named Add or delete programs. In this option, you’ve to look for the all over history of your printer.
    
  4.   When you select your entry, right click on it. Now it is the time to uninstall the driver. After uninstalling it, give a restart.
    
  5.   Now, look for your HP printer driver and install it again for the smooth work of the printer.
    
  6.   If you run a troubleshooter, then it will help you to resolve your issue immediately. The best is to run a Hardware Troubleshooter. Here are the steps to know how to run it.
    
  7.   On your computer, go to the control panel. There you will look an option to view for large icons.
    
  8.   Now, you have to for the troubleshooting. Open it and there is an option for hardware and sound. Select it.
    
  9.   When you open that folder, an option to setup a device will be there. Hit it. Then you will look a lot of instructions on your screen, follow all those to run a troubleshooter.
    

The next step is to run a printer driver in a simple mode

  1.   To run the printer fleetly, would like to} need the newest printer driver. Install it to your printer to run it effectively.
    
  2.   After putting in the updated printer driver, do a right click on the setup file. when doing therefore, you'll see the properties choice there.
    
  3.   When you click on the properties tab, AN choice of compatibility is there. Click thereon. enable it to run in a very compatibility manner. once you did it, it's higher to restart your system to implement all the specified changes. Now, check if the error is mounted or not.
    

Resolve HP Driver Install Error 1603 with the assistance of system

  1.   You will see the safety tab on the device home screen. therein tab, AN choice of Add are there. Click thereon.
    
  2.   After clicking the Add choice, AN choice can seem, termed Name. Click thereon. At the time you click it, another choice of Add are there. Hit it afterwards click on the OK button.
    
  3.   When you click the ok button, AN choice of permissions can crop up. Hit the enable choice, afterwards, click Full management and in conclusion, click on the Advanced tab.
    
  4.   Now, click on all the choices that return up with a box that you simply ought to tick by touching thereon box.
    
  5.   This checking mark isn't for all the software users, if you access Windows XP, you'll be simply asked to hit the OK button.
    
  6.   When you do all the higher than procedures, a box can seem on your screen wherever it'll raise you to click affirmative.
    
  7.   The installer package that has a slip, you would like to double-click thereon.
    

Dial Error Code Expert Toll-Free Number for Instant Assistance:

If the problem is still there than the user needs to temporarily disable the services and then reinstall the HP software. When the HP software is installed once again, then enable all the services that user disabled before. The user needs to contact experts at HP Support Assistance Number +1-866-231-0111 if the problem is still there.
Source: https://sites.google.com/view/resetchangeverizonmailpasswod/blogs/hp-printer-driver-install-error-1603

#hp printer driver install error 1603 #hp printer error 1603 #hp printer driver install errors #install hp printer driver, #hp support assistance

Khaitan

Khaitan

1635844603

पायथन में ट्रांसफॉर्मर के साथ संवादी एआई चैटबॉट

जानें कि पाइथन में प्री-ट्रेन्ड DialoGPT मॉडल के साथ संवादी प्रतिक्रियाएं उत्पन्न करने के लिए हगिंगफेस ट्रांसफॉर्मर लाइब्रेरी का उपयोग कैसे करें।

हाल के वर्षों में चैटबॉट्स ने बहुत लोकप्रियता हासिल की है, और जैसे-जैसे व्यवसाय के लिए चैटबॉट्स का उपयोग करने में रुचि बढ़ती है, शोधकर्ताओं ने संवादी एआई चैटबॉट्स को आगे बढ़ाने पर भी बहुत अच्छा काम किया है।

इस ट्यूटोरियल में, हम संवादी प्रतिक्रिया पीढ़ी के लिए पूर्व-प्रशिक्षित DialoGPT मॉडल को नियोजित करने के लिए हगिंगफेस ट्रांसफॉर्मर लाइब्रेरी का उपयोग करेंगे ।

DialoGPT एक बड़े पैमाने पर ट्यून करने योग्य तंत्रिका संवादी प्रतिक्रिया पीढ़ी मॉडल है जिसे रेडिट से निकाले गए 147M वार्तालापों पर प्रशिक्षित किया गया था, और अच्छी बात यह है कि आप स्क्रैच से प्रशिक्षण की तुलना में बेहतर प्रदर्शन प्राप्त करने के लिए इसे अपने डेटासेट के साथ ठीक कर सकते हैं।

आरंभ करने के लिए, आइए ट्रांसफॉर्मर स्थापित करें :

$ pip3 install transformers

एक नई पायथन फ़ाइल या नोटबुक खोलें और निम्न कार्य करें:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# model_name = "microsoft/DialoGPT-large"
model_name = "microsoft/DialoGPT-medium"
# model_name = "microsoft/DialoGPT-small"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

DialoGPT के तीन संस्करण हैं; छोटा, मध्यम और बड़ा। बेशक, जितना बड़ा बेहतर होगा, लेकिन अगर आप इसे अपनी मशीन पर चला रहे हैं, तो मुझे लगता है कि छोटा या मध्यम आपकी याददाश्त को बिना किसी समस्या के फिट करता है। बड़े वाले को आज़माने के लिए आप Google Colab का भी उपयोग कर सकते हैं।

लालची खोज के साथ प्रतिक्रिया उत्पन्न करना

इस खंड में, हम प्रतिक्रिया उत्पन्न करने के लिए लालची खोज एल्गोरिथ्म का उपयोग करेंगे । यही है, हम चैटबॉट प्रतिक्रिया का चयन करते हैं जिसमें प्रत्येक समय चरण पर चुने जाने की सबसे अधिक संभावना होती है।

आइए लालची खोज का उपयोग करके हमारे AI के साथ चैट करने के लिए कोड बनाएं:

# chatting 5 times with greedy search
for step in range(5):
    # take user input
    text = input(">> You:")
    # encode the input and add end of string token
    input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt")
    # concatenate new user input with chat history (if there is)
    bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids
    # generate a bot response
    chat_history_ids = model.generate(
        bot_input_ids,
        max_length=1000,
        pad_token_id=tokenizer.eos_token_id,
    )
    #print the output
    output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
    print(f"DialoGPT: {output}")

आइए इस कोड के मूल की व्याख्या करें:

  • हम सबसे पहले चैटिंग के लिए यूजर से इनपुट लेते हैं।
  • हम input_idsDialoGPT टोकननाइज़र का उपयोग करने के लिए टेक्स्ट को एन्कोड करते हैं , हम स्ट्रिंग टोकन के अंत को भी जोड़ते हैं और इसे पाइटोरच टेंसर के रूप में वापस करते हैं।
  • अगर यह पहली बार बॉट के साथ चैट कर रहा है, तो हम input_idsएक पीढ़ी के लिए सीधे अपने मॉडल को फीड करते हैं। अन्यथा, हम torch.cat()मेथड की मदद से कॉन्सटेनेशन का उपयोग करके चैट हिस्ट्री को जोड़ देते हैं ।
  • उसके बाद, हम model.generate()चैटबॉट प्रतिक्रिया उत्पन्न करने के लिए विधि का उपयोग करते हैं ।
  • अंत में, जैसा कि लौटा हुआ आउटपुट एक टोकन अनुक्रम भी है, हम अनुक्रम का उपयोग करके डीकोड करते हैं tokenizer.decode()और यह सुनिश्चित skip_special_tokensकरने के Trueलिए सेट करते हैं कि हमें कोई कष्टप्रद विशेष टोकन जैसे कि <|endoftext|>. साथ ही, चूंकि मॉडल पूरे अनुक्रम को लौटाता है, हम पिछले चैट इतिहास को छोड़ देते हैं और केवल नए जेनरेट किए गए चैटबॉट उत्तर को प्रिंट करते हैं।

नीचे बॉट के साथ एक नमूना चर्चा है:

>> You:How can you be rich so quickly?
DialoGPT: I'm not rich, I'm just a rich man.
>> You:Ok, you don't make sense, how old are you ?
DialoGPT: I'm not a rich man, I'm just a rich man.
>> You:How can you be happy ?
DialoGPT: I'm not happy, I'm just a rich man.
>> You:But they say money buys happiness!
DialoGPT: Money buys happiness, but not happiness itself.
>> You:You don't make sense again!
DialoGPT: I'm not happy, I'm just a rich man.

आप देखते हैं कि मॉडल बहुत सारी प्रतिक्रियाओं को दोहराता है, क्योंकि ये सबसे अधिक संभावना है और यह हर बार इसे चुन रहा है।

डिफ़ॉल्ट रूप से, model.generate()लालची खोज एल्गोरिथ्म का उपयोग करता है जब कोई अन्य पैरामीटर सेट नहीं किया जाता है, अगले अनुभागों में, हम इस पद्धति में कुछ तर्क जोड़ेंगे कि क्या हम पीढ़ी में सुधार कर सकते हैं।

बीम खोज के साथ प्रतिक्रिया उत्पन्न करना

बीम खोज हमें num_beamsहर समय कदम पर परिकल्पना की सबसे अधिक संभावना रखते हुए उच्च संभावना अनुक्रमों के लापता होने के जोखिम को कम करने की अनुमति देता है और फिर उन अनुक्रमों को लेकर जिनकी समग्र उच्चतम संभावना है, नीचे दिए गए कोड बीम खोज के साथ चैटबॉट प्रतिक्रियाएं उत्पन्न करेंगे:

# chatting 5 times with beam search
for step in range(5):
    # take user input
    text = input(">> You:")
    # encode the input and add end of string token
    input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt")
    # concatenate new user input with chat history (if there is)
    bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids
    # generate a bot response
    chat_history_ids = model.generate(
        bot_input_ids,
        max_length=1000,
        num_beams=3,
        early_stopping=True,
        pad_token_id=tokenizer.eos_token_id
    )
    #print the output
    output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
    print(f"DialoGPT: {output}")

सेट करते समय num_beamsके लिए 3में model.generate()विधि है, तो हम हर बार कदम पर 3 शब्दों का चयन और अनुक्रम के उच्चतम समग्र संभावना खोजने के लिए उन्हें विकसित करने के लिए जा रहे हैं, की स्थापना num_beams1 के लिए लालची खोज के समान है।

नीचे बीम खोज का उपयोग करके चैटबॉट के साथ एक नमूना चर्चा है:

>> You:How can I be rich?
DialoGPT: You can't.
>> You:Why not?
DialoGPT: Because you can't.
>> You:Then how am I supposed to live well?
DialoGPT: You can't.
>> You:So basically, you want me to be a poor guy?
DialoGPT: No, I want you to be a rich guy.
>> You:Ok how to be rich, then?
DialoGPT: I don't know.

नमूनाकरण के साथ प्रतिक्रिया उत्पन्न करना

पिछले अनुभागों में, हमने उच्चतम संभाव्यता अनुक्रम उत्पन्न करने के लिए बीम और लालची खोज का उपयोग किया था। अब यह मशीनी अनुवाद या टेक्स्ट सारांश जैसे कार्यों के लिए बहुत अच्छा है जहां आउटपुट अनुमानित है। हालाँकि, चैटबॉट्स की तरह ओपन-एंडेड पीढ़ी के लिए यह सबसे अच्छा विकल्प नहीं है।

एक बेहतर पीढ़ी के लिए, हमें कुछ यादृच्छिकता पेश करने की आवश्यकता है जहां हम संभावनाओं के आधार पर उम्मीदवार अनुक्रमों की एक विस्तृत श्रृंखला से नमूना लेते हैं:

# chatting 5 times with sampling
for step in range(5):
    # take user input
    text = input(">> You:")
    # encode the input and add end of string token
    input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt")
    # concatenate new user input with chat history (if there is)
    bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids
    # generate a bot response
    chat_history_ids = model.generate(
        bot_input_ids,
        max_length=1000,
        do_sample=True,
        top_k=0,
        pad_token_id=tokenizer.eos_token_id
    )
    #print the output
    output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
    print(f"DialoGPT: {output}")

इस बार, हमने नमूनाकरण के लिए सेट do_sampleकिया Trueहै, और हम यह इंगित करने के top_kलिए सेट हैं 0कि हम सभी संभावित संभावनाओं का चयन कर रहे हैं, हम बाद में top_kपैरामीटर पर चर्चा करेंगे ।

यहाँ इन मापदंडों के साथ बातचीत है:

>> You:Hello, how can I be rich?
DialoGPT: You can't
>> You:Really? then how others get rich?
DialoGPT: They work as money artists
>> You:How to be a money artist?
DialoGPT: I don't know, I don't have enough knowledge on artists
>> You:Alright, thank you!
DialoGPT: No problem, if you need tips pm me.
>> You:Great, you're my savior!
DialoGPT: Thank you for helping a stranger out like me!

स्पष्ट रूप से कुछ सुधार हैं। हालांकि, कम संभावनाओं वाले अनुक्रमों की एक विस्तृत सूची पर नमूना लेने से यादृच्छिक पीढ़ी हो सकती है (जैसा कि आप अंतिम वाक्य में देखते हैं)।

इसे और बेहतर बनाने के लिए, हम यह कर सकते हैं:

  • नमूनाकरण कम करें temperature, जिससे हमें कम संभावना वाले शब्दों को चुनने की संभावना कम करने में मदद मिलती है और उच्च संभावना वाले शब्दों को चुनने की संभावना बढ़ जाती है।
  • सभी संभावित घटनाओं को चुनने के बजाय टॉप-के नमूने का उपयोग करें, इससे हमें कम संभावना वाले शब्दों को चुनने से रोकने में मदद मिलेगी।
# chatting 5 times with Top K sampling & tweaking temperature
for step in range(5):
    # take user input
    text = input(">> You:")
    # encode the input and add end of string token
    input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt")
    # concatenate new user input with chat history (if there is)
    bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids
    # generate a bot response
    chat_history_ids = model.generate(
        bot_input_ids,
        max_length=1000,
        do_sample=True,
        top_k=100,
        temperature=0.75,
        pad_token_id=tokenizer.eos_token_id
    )
    #print the output
    output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
    print(f"DialoGPT: {output}")

अब, हम संभाव्यता द्वारा अवरोही क्रम में शीर्ष शब्दों से नमूना लेने के लिए सेट top_kकरते हैं । हम उच्च संभावना वाले शब्दों को चुनने का एक उच्च मौका देने के लिए (डिफ़ॉल्ट है ) पर भी सेट करते हैं , तापमान को लालची खोज के समान ही सेट करते हैं, इसे अनंत पर सेट करना पूरी तरह से यादृच्छिक के समान है।100100temperature0.751.00.0

यहाँ इन मापदंडों के साथ एक चर्चा है:

>> You:How can I get rich?
DialoGPT: I'd also like to know this.
>> You:So you don't know it?
DialoGPT: I know it, but it doesn't matter.
>> You:Really? money matters, man!
DialoGPT: Well, money is money and I gotta get my money.
>> You:Yeah how to get them?
DialoGPT: Well, that's a question. I'd like to know more, just for curiosity's sake, to know what other people have said about how I should get rich.
>> You:Some robots like you told me you have to buy gold, silver or stocks.
DialoGPT: I think my wallet is empty.

जैसा कि आप देख सकते हैं, यह अब बहुत बेहतर है, बेझिझक ट्विक करें temperatureऔर top_kपैरामीटर देखें और देखें कि क्या यह इसमें सुधार कर सकता है।

न्यूक्लियस सैंपलिंग

न्यूक्लियस सैंपलिंग या टॉप-पी सैंपलिंग उन सबसे छोटे संभव शब्दों में से चुनता है जिनकी संचयी संभावना pहमारे द्वारा निर्धारित पैरामीटर से अधिक होती है ।

टॉप-पी सैंपलिंग का उपयोग करते हुए एक उदाहरण नीचे दिया गया है:

# chatting 5 times with nucleus sampling & tweaking temperature
for step in range(5):
    # take user input
    text = input(">> You:")
    # encode the input and add end of string token
    input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt")
    # concatenate new user input with chat history (if there is)
    bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids
    # generate a bot response
    chat_history_ids = model.generate(
        bot_input_ids,
        max_length=1000,
        do_sample=True,
        top_p=0.95,
        top_k=0,
        temperature=0.75,
        pad_token_id=tokenizer.eos_token_id
    )
    #print the output
    output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
    print(f"DialoGPT: {output}")

हमने टॉप-के सैंपलिंग को अक्षम top_kकरने के 0लिए सेट किया है, लेकिन आप दोनों विधियों का उपयोग कर सकते हैं जो बेहतर काम करती हैं। यहाँ एक चैट है:

>> You:How can I be rich fast?
DialoGPT: Inflation and free money
>> You:lol, but that takes time, I need fast money.
DialoGPT: Your post makes no sense. You just need a lot of money to be rich fast
>> You:Ok how to get a lot of money?
DialoGPT: Buy gold and then trade it for money.
>> You:Hmm, I got your point, thanks.
DialoGPT: No problem. You have to have money to buy gold, but its only good for trading it.
>> You:Yeah, I understand, thank you so much.
DialoGPT: Ok, let me know if you need help

अब चैटबॉट कई मामलों में स्पष्ट रूप से समझ में आता है।

अब एक से अधिक चैटबॉट प्रतिक्रिया उत्पन्न करने के लिए कुछ कोड जोड़ते हैं, और फिर हम चुनते हैं कि अगले इनपुट में किस प्रतिक्रिया को शामिल करना है:

# chatting 5 times with nucleus & top-k sampling & tweaking temperature & multiple
# sentences
for step in range(5):
    # take user input
    text = input(">> You:")
    # encode the input and add end of string token
    input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt")
    # concatenate new user input with chat history (if there is)
    bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids
    # generate a bot response
    chat_history_ids_list = model.generate(
        bot_input_ids,
        max_length=1000,
        do_sample=True,
        top_p=0.95,
        top_k=50,
        temperature=0.75,
        num_return_sequences=5,
        pad_token_id=tokenizer.eos_token_id
    )
    #print the outputs
    for i in range(len(chat_history_ids_list)):
      output = tokenizer.decode(chat_history_ids_list[i][bot_input_ids.shape[-1]:], skip_special_tokens=True)
      print(f"DialoGPT {i}: {output}")
    choice_index = int(input("Choose the response you want for the next input: "))
    chat_history_ids = torch.unsqueeze(chat_history_ids_list[choice_index], dim=0)

मैंने एक बार में 5 वाक्यों को वापस num_return_sequencesकरने के 5लिए निर्धारित किया है, हमें एक को चुनना होगा जिसे अगले अनुक्रम में शामिल किया जाएगा। यहां बताया गया है कि यह कैसे चला गया:

>> You:Hello, friend.
DialoGPT 0: Hey there!
DialoGPT 1: I am your friend, and I am also your friend.
DialoGPT 2: Hello. :
DialoGPT 3: Hiya! How are you?
DialoGPT 4: Hello. I like your username.
Choose the response you want for the next input: 3
>> You:Fine, thanks, what about you?
DialoGPT 0: Good. Not much to do.
DialoGPT 1: Not much, you?
DialoGPT 2: Fine, thanks.
DialoGPT 3: I'm okay, thank you for asking.
DialoGPT 4: I'm fine too.
Choose the response you want for the next input: 2
>> You:Can you tell me how can I get rich quickly?
DialoGPT 0: Yes, I can!
DialoGPT 1: Yes, just look up how to get rich on Google.
DialoGPT 2: Just keep doing your thing. You'll get rich.
DialoGPT 3: No, I can't.
DialoGPT 4: no, i have no money
Choose the response you want for the next input: 1
>> You:Yeah, but I thought you have an answer...
DialoGPT 0: I'm getting married in the morning.
DialoGPT 1: How do you get rich?
DialoGPT 2: I think there are many good answers, but I could send you one, if you want.
DialoGPT 3: I will, thanks!
DialoGPT 4: I thought you have an answer.
Choose the response you want for the next input: 2
>> You:Yes, please send me.
DialoGPT 0: You can send me link.
DialoGPT 1: I am very interested
DialoGPT 2: I sent you a PM
DialoGPT 3: I'll send you a PM
DialoGPT 4: I am always interested in new ideas.
Choose the response you want for the next input: 2

निष्कर्ष

और आप वहां जाएं, मुझे आशा है कि इस ट्यूटोरियल ने आपको DialoGPT और इसी तरह के मॉडल पर टेक्स्ट जेनरेट करने में मदद की। टेक्स्ट जेनरेट करने के तरीके के बारे में अधिक जानकारी के लिए, मैं आपको ट्रांसफॉर्मर्स गाइड के साथ टेक्स्ट जेनरेट करने का तरीका पढ़ने की अत्यधिक सलाह देता हूं ।

यह देखने के लिए कि क्या आप बॉट को बेहतर प्रदर्शन कर सकते हैं, मैं आपको मापदंडों को बदलना छोड़ दूँगा।

साथ ही, आप इसे टेक्स्ट-टू-स्पीच और स्पीच-टू-टेक्स्ट ट्यूटोरियल्स के साथ जोड़कर एक वर्चुअल असिस्टेंट जैसे एलेक्सा , सिरी , कोरटाना आदि बना सकते हैं।

#python #chatbot #ai 

渚  直樹

渚 直樹

1636598700

Pythonでリストを昇順および降順でソートする

リストを昇順および降順でソートするためのPythonプログラム。このPythonチュートリアルでは、リストの要素をPythonで昇順と降順で並べ替える方法を紹介します。

pythonの組み込みメソッド名sort()を使用します。これは、リストの要素/オブジェクトを昇順および降順で並べ替えるために使用されます。

ソートメソッドの基本構文:

 list.sort()

リストを昇順および降順でソートするためのPythonプログラム

  • リスト要素を昇順でソートするPythonプログラム
  • リスト要素を降順でソートするPythonプログラム

リスト要素を昇順でソートするPythonプログラム

# List of integers
num = [100, 200, 500, 600, 300]
 
# sorting and printing 
num.sort()
 
#print
print(num)
 
# List of float numbers
fnum = [100.43, 50.72, 90.65, 16.00, 04.41]
 
# sorting and printing
fnum.sort()
 
#print
print(fnum)
 
# List of strings 
str = ["Test", "My", "Word", "Tag", "Has"]
 
# sorting and  printing
str.sort()
 
#print
print(str)

Pythonプログラムを実行すると、出力は次のようになります。

[100、200、300、500、600]
[4.41、16.0、50.72、90.65、100.43]
['Has'、 'My'、 'Tag'、 'Test'、 'Word']

上で知っているように、リスト要素を昇順で並べ替える方法。次に、sort()メソッドを使用してリストを降順で並べ替える方法を説明します。

sort()メソッドを使用して引数としてreverse = Trueを渡し、リスト要素を降順で並べ替えます。

リスト要素を降順でソートする次のプログラムを見ることができます。

リスト要素を降順でソートするPythonプログラム

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# List of integers
num = [100, 200, 500, 600, 300]
 
# sorting and printing 
num.sort(reverse=True)
 
#print
print(num)
 
# List of float numbers
fnum = [100.43, 50.72, 90.65, 16.00, 04.41]
 
# sorting and printing
fnum.sort(reverse=True)
 
#print
print(fnum)
 
# List of strings 
str = ["Test", "My", "Word", "Tag", "Has"]
 
# sorting and  printing
str.sort(reverse=True)
 
#print
print(str)

プログラムの実行後、出力は次のようになります。

[600、500、300、200、100] 
[100.43、90.65、50.72、16.0、4.41] 
['Word'、 'Test'、 'Tag'、 'My'、 'Has']

リンク: https://www.tutsmake.com/python-program-to-sort-list-in-ascending-and-descending-order/

#python 

Hoang  Kim

Hoang Kim

1635843812

Chatbot AI hội thoại với Máy biến áp được đào tạo trước bằng Python

Tìm hiểu cách sử dụng thư viện máy biến áp Huggingface để tạo phản hồi hội thoại bằng mô hình DialoGPT được đào tạo trước bằng Python.

Chatbots đã trở nên phổ biến trong những năm gần đây và khi mối quan tâm ngày càng tăng trong việc sử dụng chatbots cho kinh doanh, các nhà nghiên cứu cũng đã làm rất tốt trong việc phát triển các chatbot AI đàm thoại.

Trong hướng dẫn này, chúng tôi sẽ sử dụng thư viện máy biến áp Huggingface để sử dụng mô hình DialoGPT đã được đào tạo trước để tạo phản hồi hội thoại.

DialoGPT là một mô hình tạo phản hồi hội thoại thần kinh có thể điều chỉnh quy mô lớn được đào tạo trên 147 triệu cuộc hội thoại được trích xuất từ ​​Reddit và điều tốt là bạn có thể tinh chỉnh nó với bộ dữ liệu của mình để đạt được hiệu suất tốt hơn so với đào tạo từ đầu.

Để bắt đầu, hãy cài đặt máy biến áp :

$ pip3 install transformers

Mở tệp hoặc sổ ghi chép Python mới và thực hiện như sau:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# model_name = "microsoft/DialoGPT-large"
model_name = "microsoft/DialoGPT-medium"
# model_name = "microsoft/DialoGPT-small"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

Có ba phiên bản của DialoGPT; nhỏ, vừa và lớn. Tất nhiên, càng lớn càng tốt, nhưng nếu bạn chạy điều này trên máy của mình, tôi nghĩ nhỏ hoặc trung bình phù hợp với bộ nhớ của bạn mà không có vấn đề gì. Bạn cũng có thể sử dụng Google Colab để thử cái lớn.

Tạo phản hồi bằng Tìm kiếm tham lam

Trong phần này, chúng tôi sẽ sử dụng thuật toán tìm kiếm tham lam để tạo phản hồi. Đó là, chúng tôi chọn phản hồi chatbot có xác suất cao nhất được chọn trên mỗi bước thời gian.

Hãy tạo mã để trò chuyện với AI của chúng tôi bằng cách sử dụng tìm kiếm tham lam:

# chatting 5 times with greedy search
for step in range(5):
    # take user input
    text = input(">> You:")
    # encode the input and add end of string token
    input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt")
    # concatenate new user input with chat history (if there is)
    bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids
    # generate a bot response
    chat_history_ids = model.generate(
        bot_input_ids,
        max_length=1000,
        pad_token_id=tokenizer.eos_token_id,
    )
    #print the output
    output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
    print(f"DialoGPT: {output}")

Hãy giải thích cốt lõi của mã này:

  • Đầu tiên chúng tôi lấy thông tin đầu vào từ người dùng để trò chuyện.
  • Chúng tôi mã hóa văn bản input_idsbằng cách sử dụng trình mã hóa DialoGPT, chúng tôi cũng nối vào cuối chuỗi mã thông báo và trả về nó dưới dạng bộ căng Pytorch.
  • Nếu đây là lần đầu tiên trò chuyện với bot, thì chúng tôi sẽ trực tiếp cung cấp input_idscho mô hình của mình trong một thế hệ. Nếu không, chúng tôi nối lịch sử trò chuyện bằng cách nối với sự trợ giúp của torch.cat()phương thức.
  • Sau đó, chúng tôi sử dụng model.generate()phương pháp tạo phản hồi chatbot.
  • Cuối cùng, như sản lượng trở lại là một chuỗi tokenized quá, chúng tôi giải mã trình tự sử dụng tokenizer.decode()và thiết lập skip_special_tokensđể Trueđảm bảo chúng tôi không thấy bất kỳ đặc biệt gây phiền nhiễu mã thông báo như <|endoftext|>. Ngoài ra, vì mô hình trả về toàn bộ chuỗi, chúng tôi bỏ qua lịch sử trò chuyện trước đó và chỉ in câu trả lời chatbot mới được tạo.

Dưới đây là một cuộc thảo luận mẫu với bot:

>> You:How can you be rich so quickly?
DialoGPT: I'm not rich, I'm just a rich man.
>> You:Ok, you don't make sense, how old are you ?
DialoGPT: I'm not a rich man, I'm just a rich man.
>> You:How can you be happy ?
DialoGPT: I'm not happy, I'm just a rich man.
>> You:But they say money buys happiness!
DialoGPT: Money buys happiness, but not happiness itself.
>> You:You don't make sense again!
DialoGPT: I'm not happy, I'm just a rich man.

Bạn thấy mô hình lặp lại rất nhiều phản hồi, vì đây là xác suất cao nhất và nó luôn chọn nó.

Theo mặc định, model.generate()sử dụng thuật toán tìm kiếm tham lam khi không có tham số nào khác được đặt, trong các phần tiếp theo, chúng tôi sẽ thêm một số đối số vào phương thức này để xem liệu chúng tôi có thể cải thiện việc tạo không.

Tạo phản hồi với Tìm kiếm chùm

Tìm kiếm theo chùm cho phép chúng tôi giảm nguy cơ bỏ lỡ các chuỗi có xác suất cao bằng cách giữ lại num_beamscác giả thuyết có khả năng xảy ra nhất ở mỗi bước thời gian và sau đó lấy các chuỗi có xác suất cao nhất tổng thể, đoạn mã dưới đây sẽ tạo ra các phản hồi của chatbot với tìm kiếm theo chùm:

# chatting 5 times with beam search
for step in range(5):
    # take user input
    text = input(">> You:")
    # encode the input and add end of string token
    input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt")
    # concatenate new user input with chat history (if there is)
    bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids
    # generate a bot response
    chat_history_ids = model.generate(
        bot_input_ids,
        max_length=1000,
        num_beams=3,
        early_stopping=True,
        pad_token_id=tokenizer.eos_token_id
    )
    #print the output
    output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
    print(f"DialoGPT: {output}")

Khi đặt num_beamsthành 3trong model.generate()phương thức, chúng tôi sẽ chọn 3 từ ở mỗi bước và phát triển chúng để tìm xác suất tổng thể cao nhất của chuỗi, đặt num_beamsthành 1 cũng giống như tìm kiếm tham lam.

Dưới đây là một cuộc thảo luận mẫu với chatbot bằng cách sử dụng tìm kiếm chùm:

>> You:How can I be rich?
DialoGPT: You can't.
>> You:Why not?
DialoGPT: Because you can't.
>> You:Then how am I supposed to live well?
DialoGPT: You can't.
>> You:So basically, you want me to be a poor guy?
DialoGPT: No, I want you to be a rich guy.
>> You:Ok how to be rich, then?
DialoGPT: I don't know.

Tạo phản hồi bằng lấy mẫu

Trong các phần trước, chúng tôi đã sử dụng tìm kiếm chùm và tham lam để tạo ra chuỗi xác suất cao nhất. Giờ đây, điều đó thật tuyệt vời cho các tác vụ như dịch máy hoặc tóm tắt văn bản trong đó kết quả đầu ra có thể dự đoán được. Tuy nhiên, nó không phải là lựa chọn tốt nhất cho thế hệ kết thúc mở như trong chatbots.

Để có một thế hệ tốt hơn, chúng tôi cần đưa ra một số ngẫu nhiên trong đó chúng tôi lấy mẫu từ một loạt các trình tự ứng viên dựa trên xác suất:

# chatting 5 times with sampling
for step in range(5):
    # take user input
    text = input(">> You:")
    # encode the input and add end of string token
    input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt")
    # concatenate new user input with chat history (if there is)
    bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids
    # generate a bot response
    chat_history_ids = model.generate(
        bot_input_ids,
        max_length=1000,
        do_sample=True,
        top_k=0,
        pad_token_id=tokenizer.eos_token_id
    )
    #print the output
    output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
    print(f"DialoGPT: {output}")

Lần này, chúng tôi đặt do_sampleđể Truelấy mẫu và chúng tôi đặt top_kđể 0chỉ ra rằng chúng tôi đang chọn tất cả các xác suất có thể xảy ra, sau đó chúng ta sẽ thảo luận về top_ktham số.

Đây là một cuộc trò chuyện với các thông số này:

>> You:Hello, how can I be rich?
DialoGPT: You can't
>> You:Really? then how others get rich?
DialoGPT: They work as money artists
>> You:How to be a money artist?
DialoGPT: I don't know, I don't have enough knowledge on artists
>> You:Alright, thank you!
DialoGPT: No problem, if you need tips pm me.
>> You:Great, you're my savior!
DialoGPT: Thank you for helping a stranger out like me!

Rõ ràng là có một số cải tiến. Tuy nhiên, việc lấy mẫu trên một danh sách rộng các chuỗi với xác suất thấp có thể dẫn đến việc tạo ngẫu nhiên (như bạn thấy trong câu cuối cùng).

Để cải thiện nó hơn nữa, chúng tôi có thể:

  • Hạ thấp mẫu temperature, điều đó giúp chúng tôi giảm khả năng chọn các từ có xác suất thấp và tăng khả năng chọn các từ có xác suất cao.
  • Sử dụng lấy mẫu Top-k thay vì chọn tất cả các lần xuất hiện có thể xảy ra, điều này sẽ giúp chúng tôi loại bỏ các từ có xác suất thấp để được chọn.
# chatting 5 times with Top K sampling & tweaking temperature
for step in range(5):
    # take user input
    text = input(">> You:")
    # encode the input and add end of string token
    input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt")
    # concatenate new user input with chat history (if there is)
    bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids
    # generate a bot response
    chat_history_ids = model.generate(
        bot_input_ids,
        max_length=1000,
        do_sample=True,
        top_k=100,
        temperature=0.75,
        pad_token_id=tokenizer.eos_token_id
    )
    #print the output
    output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
    print(f"DialoGPT: {output}")

Bây giờ, chúng ta thiết lập top_kđể 100lấy mẫu từ đỉnh 100từ được sắp xếp descendingly bởi xác suất. Chúng tôi cũng đặt temperaturethành 0.75(mặc định là 1.0) để có cơ hội chọn các từ có xác suất cao hơn, đặt nhiệt độ 0.0giống như tìm kiếm tham lam, đặt nhiệt độ thành vô cùng giống như hoàn toàn ngẫu nhiên.

Đây là một cuộc thảo luận với các thông số này:

>> You:How can I get rich?
DialoGPT: I'd also like to know this.
>> You:So you don't know it?
DialoGPT: I know it, but it doesn't matter.
>> You:Really? money matters, man!
DialoGPT: Well, money is money and I gotta get my money.
>> You:Yeah how to get them?
DialoGPT: Well, that's a question. I'd like to know more, just for curiosity's sake, to know what other people have said about how I should get rich.
>> You:Some robots like you told me you have to buy gold, silver or stocks.
DialoGPT: I think my wallet is empty.

Như bạn có thể thấy, bây giờ nó đã tốt hơn nhiều, hãy thoải mái tinh chỉnh temperaturetop_kcác thông số và xem liệu nó có thể cải thiện nó hay không.

Lấy mẫu hạt nhân

Lấy mẫu hạt nhân hoặc lấy mẫu Top-p chọn từ các từ nhỏ nhất có thể có xác suất tích lũy vượt quá tham số pchúng tôi đặt.

Dưới đây là một ví dụ sử dụng lấy mẫu Top-p:

# chatting 5 times with nucleus sampling & tweaking temperature
for step in range(5):
    # take user input
    text = input(">> You:")
    # encode the input and add end of string token
    input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt")
    # concatenate new user input with chat history (if there is)
    bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids
    # generate a bot response
    chat_history_ids = model.generate(
        bot_input_ids,
        max_length=1000,
        do_sample=True,
        top_p=0.95,
        top_k=0,
        temperature=0.75,
        pad_token_id=tokenizer.eos_token_id
    )
    #print the output
    output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
    print(f"DialoGPT: {output}")

Chúng tôi đặt top_kđể 0tắt lấy mẫu Top-k, nhưng bạn có thể sử dụng cả hai phương pháp có xu hướng hoạt động tốt hơn. Đây là một cuộc trò chuyện:

>> You:How can I be rich fast?
DialoGPT: Inflation and free money
>> You:lol, but that takes time, I need fast money.
DialoGPT: Your post makes no sense. You just need a lot of money to be rich fast
>> You:Ok how to get a lot of money?
DialoGPT: Buy gold and then trade it for money.
>> You:Hmm, I got your point, thanks.
DialoGPT: No problem. You have to have money to buy gold, but its only good for trading it.
>> You:Yeah, I understand, thank you so much.
DialoGPT: Ok, let me know if you need help

Giờ đây, chatbot rõ ràng có ý nghĩa trong nhiều trường hợp.

Bây giờ, hãy thêm một số mã để tạo nhiều hơn một phản hồi chatbot và sau đó chúng tôi chọn phản hồi nào sẽ bao gồm trong đầu vào tiếp theo:

# chatting 5 times with nucleus & top-k sampling & tweaking temperature & multiple
# sentences
for step in range(5):
    # take user input
    text = input(">> You:")
    # encode the input and add end of string token
    input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt")
    # concatenate new user input with chat history (if there is)
    bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids
    # generate a bot response
    chat_history_ids_list = model.generate(
        bot_input_ids,
        max_length=1000,
        do_sample=True,
        top_p=0.95,
        top_k=50,
        temperature=0.75,
        num_return_sequences=5,
        pad_token_id=tokenizer.eos_token_id
    )
    #print the outputs
    for i in range(len(chat_history_ids_list)):
      output = tokenizer.decode(chat_history_ids_list[i][bot_input_ids.shape[-1]:], skip_special_tokens=True)
      print(f"DialoGPT {i}: {output}")
    choice_index = int(input("Choose the response you want for the next input: "))
    chat_history_ids = torch.unsqueeze(chat_history_ids_list[choice_index], dim=0)

Tôi đã thiết lập num_return_sequencesđể 5trả lại 5 câu cùng một lúc, chúng ta phải chọn một câu sẽ có trong chuỗi tiếp theo. Dưới đây là làm thế nào nó đi:

>> You:Hello, friend.
DialoGPT 0: Hey there!
DialoGPT 1: I am your friend, and I am also your friend.
DialoGPT 2: Hello. :
DialoGPT 3: Hiya! How are you?
DialoGPT 4: Hello. I like your username.
Choose the response you want for the next input: 3
>> You:Fine, thanks, what about you?
DialoGPT 0: Good. Not much to do.
DialoGPT 1: Not much, you?
DialoGPT 2: Fine, thanks.
DialoGPT 3: I'm okay, thank you for asking.
DialoGPT 4: I'm fine too.
Choose the response you want for the next input: 2
>> You:Can you tell me how can I get rich quickly?
DialoGPT 0: Yes, I can!
DialoGPT 1: Yes, just look up how to get rich on Google.
DialoGPT 2: Just keep doing your thing. You'll get rich.
DialoGPT 3: No, I can't.
DialoGPT 4: no, i have no money
Choose the response you want for the next input: 1
>> You:Yeah, but I thought you have an answer...
DialoGPT 0: I'm getting married in the morning.
DialoGPT 1: How do you get rich?
DialoGPT 2: I think there are many good answers, but I could send you one, if you want.
DialoGPT 3: I will, thanks!
DialoGPT 4: I thought you have an answer.
Choose the response you want for the next input: 2
>> You:Yes, please send me.
DialoGPT 0: You can send me link.
DialoGPT 1: I am very interested
DialoGPT 2: I sent you a PM
DialoGPT 3: I'll send you a PM
DialoGPT 4: I am always interested in new ideas.
Choose the response you want for the next input: 2

Phần kết luận

Và bạn hiểu rồi, tôi hy vọng hướng dẫn này đã giúp bạn cách tạo văn bản trên DialoGPT và các mô hình tương tự. Để biết thêm thông tin về cách tạo văn bản, tôi thực sự khuyên bạn nên đọc hướng dẫn Cách tạo văn bản bằng Transformers .

Tôi sẽ để bạn điều chỉnh các thông số để xem liệu bạn có thể làm cho bot hoạt động tốt hơn hay không.

Ngoài ra, bạn có thể kết hợp điều này với các hướng dẫn chuyển văn bản thành giọng nói và chuyển lời nói thành văn bản để xây dựng một trợ lý ảo như Alexa , Siri , Cortana , v.v.

#python #ai #chatbot