48 lines
1.5 KiB
Python
48 lines
1.5 KiB
Python
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# import requests
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#
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# API_TOKEN = "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"
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# API_URL = "https://api-inference.huggingface.co/models/google/mt5-base"
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#
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# headers = {
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# "Authorization": f"Bearer {API_TOKEN}",
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# "Content-Type": "application/json"
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# }
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#
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# def query_mT5(prompt):
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# payload = {
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# "inputs": prompt,
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# "parameters": {
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# "max_length": 100,
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# "do_sample": True,
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# "temperature": 0.7
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# }
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# }
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# response = requests.post(API_URL, headers=headers, json=payload)
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# return response.json()
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#
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# # Пример использования
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# result = query_mT5("Aké sú účinné lieky na horúčku?")
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# print("Ответ от mT5:", result)
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from transformers import AutoTokenizer, MT5ForConditionalGeneration
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tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
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model = MT5ForConditionalGeneration.from_pretrained("google/mt5-small")
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# training
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input_ids = tokenizer("The <extra_id_0> walks in <extra_id_1> park", return_tensors="pt").input_ids
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labels = tokenizer("<extra_id_0> cute dog <extra_id_1> the <extra_id_2>", return_tensors="pt").input_ids
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outputs = model(input_ids=input_ids, labels=labels)
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loss = outputs.loss
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logits = outputs.logits
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# inference
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input_ids = tokenizer(
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"summarize: studies have shown that owning a dog is good for you", return_tensors="pt"
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).input_ids # Batch size 1
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outputs = model.generate(input_ids, max_new_tokens=50)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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# studies have shown that owning a dog is good for you.
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