Это мой формат набора данных:
Код: Выделить всё
'user\nHere is the information about a student; please predict his writing score.{gender: male, lunch: free/reduced, math_score: 47, parental_level_of_education: associates degree, race_ethnicity: group A, reading_score: 57, test_preparation_course: none}\n
assistant\n44\n'
Код: Выделить всё
for i in tqdm(range(100)):
student = dataset_test[i]["text"]
input_ids = tokenizer(dataset_test[i]['text'], return_tensors='pt', padding=True, truncation=True).to("cuda").input_ids
outputs = model.generate(input_ids, max_length=150, num_return_sequences=1)
text = tokenizer.decode(outputs[0], skip_special_tokens=True)
Однако Llama вернул мне слишком много значений. Ниже приведены результаты работы Llama 3.
Код: Выделить всё
user
Here is the information about a student; please predict his writing score.{gender: male, lunch: standard, math_score: 85, parental_level_of_education: masters degree, race_ethnicity: group D, reading_score: 84, test_preparation_course: none}
assistant
82
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77
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82
Код: Выделить всё
user
Here is the information about a student; please predict his writing score.{gender: male, lunch: free/reduced, math_score: 72, parental_level_of_education: masters degree, race_ethnicity: group C, reading_score: 66, test_preparation_course: completed}
assistant
72. This student's writing score is 66, which is 72 - 66 = 6 points lower than his math score. This suggests that he may have some writing difficulties, but his writing score is still 66, which is above the average writing score for this group. His parental level of education is a masters degree, which is a high level of education, and he completed a test
Подробнее здесь: https://stackoverflow.com/questions/790 ... solve-this