Train modell for Question Answering
I’m trying to fine-tune a model for my thesis using the following dataset for question-and-answer tasks:SzegedAI/MILQA at main, in the following code: `import os
import json
import torch
from transformers import DefaultDataCollator, AutoModelForQuestionAnswering, AutoTokenizer, Trainer, TrainingArguments, TrainerCallback
import matplotlib.pyplot as plt
from torch.utils.data import Dataset

def process_data(json_file):
    with open(json_file, "r", encoding="utf-8") as f:
        data = json.load(f)

    inputs = []
    for item in data["data"]:
        for paragraph in item["paragraphs"]:
            context = paragraph["context"]
            qa_list = [{'Question': qa["question"]} for qa in paragraph["qas"]]
            inputs.append({'Context': context, 'Questions_Answers': qa_list})
    return inputs
class PlotCallback(TrainerCallback):
    def __init__(self):
        self.train_losses = []
        self.eval_losses = []
        self.eval_steps = []

    def on_log(self, args, state, control, logs=None, **kwargs):
        if state.is_world_process_zero:
            if 'loss' in logs:
                self.train_losses.append((state.global_step, logs['loss']))
            if 'eval_loss' in logs:
                self.eval_losses.append((state.global_step, logs['eval_loss']))
                self.eval_steps.append(state.global_step)

    def on_train_end(self, args, state, control, **kwargs):
        # Training loss diagram
        plt.figure(figsize=(10, 6))
        plt.plot([step_loss[0] for step_loss in self.train_losses], [step_loss[1] for step_loss in self.train_losses], label='Training Loss', color='blue')
        plt.title('Training Loss over Training Steps')
        plt.xlabel('Training Steps')
        plt.ylabel('Loss')
        plt.legend()
        plt.grid(True)
        plt.show()

class MyDataCollator(DefaultDataCollator):
    def __init__(self, tokenizer):
        super().__init__()
        self.tokenizer = tokenizer

    def collate_batch(self, examples):
        input_ids = []
        attention_masks = []
        labels = []

        for example in examples:
            context_inputs = self.tokenizer(example['Context'], return_tensors="pt", padding=True, truncation=True)
            context_input_ids = context_inputs["input_ids"]
            context_attention_mask = context_inputs["attention_mask"]

            for qa in example['Questions_Answers']:
                question_inputs = self.tokenizer(qa['Question'], return_tensors="pt", padding=True, truncation=True)
                question_input_ids = question_inputs["input_ids"]
                question_attention_mask = question_inputs["attention_mask"]

                input_ids.append(torch.cat([context_input_ids.squeeze(0), question_input_ids.squeeze(0)], dim=0))
                attention_masks.append(torch.cat([context_attention_mask.squeeze(0), question_attention_mask.squeeze(0)], dim=0))
                labels.append(question_input_ids.squeeze(0))  # Use question_input_ids as labels

        return {
            "input_ids": torch.nn.utils.rnn.pad_sequence(input_ids, batch_first=True),
            "attention_mask": torch.nn.utils.rnn.pad_sequence(attention_masks, batch_first=True),
            "labels": torch.nn.utils.rnn.pad_sequence(labels, batch_first=True)
        }

class MyDataset(Dataset):
    def __init__(self, batch):
        self.inputs = batch['input_ids']
        self.attention_masks = batch['attention_mask']
        self.labels = batch['labels']

    def __len__(self):
        return len(self.inputs)

    def __getitem__(self, idx):
        return {
            'input_ids': self.inputs[idx],
            'attention_mask': self.attention_masks[idx],
            'labels': self.labels[idx],
        }

with open("train.MILQA-2023-03-27.squad.s.json", "r", encoding="utf-8") as f:
    data = json.load(f)
with open("test.MILQA-2023-03-27.squad.s.json", "r", encoding="utf-8") as f:
    eval_data = json.load(f)
model_name = 'TinyLlama/TinyLlama-1.1B-Chat-v1.0'
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

data_collator = MyDataCollator(tokenizer)

train_inputs = process_data("train.MILQA-2023-03-27.squad.s.json")
eval_inputs = process_data("test.MILQA-2023-03-27.squad.s.json")

train_batch = data_collator.collate_batch(train_inputs)
eval_batch = data_collator.collate_batch(eval_inputs)

eval_dataset = MyDataset(eval_batch)
train_dataset = MyDataset(train_batch)


output_dir = "./finetuned_model"
if not os.path.exists(output_dir):
    os.makedirs(output_dir)

training_args = TrainingArguments(
    output_dir=output_dir,
    overwrite_output_dir=True,
    num_train_epochs=2,
    learning_rate=2e-4,
    per_device_train_batch_size=2,
    warmup_ratio=0.1,
    lr_scheduler_type="linear",
    save_strategy="epoch",
    eval_steps=500,
    save_steps=500,
)

trainer = Trainer(
    model=model,
    args=training_args,
    data_collator=data_collator,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    callbacks=[PlotCallback()],
)

trainer.train()

tokenizer.save_pretrained(output_dir)However, I’m encountering the following errors:C:\Users\Levente\Desktop\minigpt\.venv\Scripts\python.exe C:\Users\Levente\Desktop\minigpt\train.py 
2024-05-05 12:48:37.957206: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-05-05 12:48:38.591477: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
Some weights of LlamaForQuestionAnswering were not initialized from the model checkpoint at TinyLlama/TinyLlama-1.1B-Chat-v1.0 and are newly initialized: ['embed_tokens.weight', 'layers.0.input_layernorm.weight', 'layers.0.mlp.down_proj.weight', 'layers.0.mlp.gate_proj.weight', 'layers.0.mlp.up_proj.weight', 'layers.0.post_attention_layernorm.weight', 'layers.0.self_attn.k_proj.weight', 'layers.0.self_attn.o_proj.weight', 'layers.0.self_attn.q_proj.weight', 'layers.0.self_attn.v_proj.weight', 'layers.1.input_layernorm.weight', 'layers.1.mlp.down_proj.weight', 'layers.1.mlp.gate_proj.weight', 'layers.1.mlp.up_proj.weight', 'layers.1.post_attention_layernorm.weight', 'layers.1.self_attn.k_proj.weight', 'layers.1.self_attn.o_proj.weight', 'layers.1.self_attn.q_proj.weight', 'layers.1.self_attn.v_proj.weight', 'layers.10.input_layernorm.weight', 'layers.10.mlp.down_proj.weight', 'layers.10.mlp.gate_proj.weight', 'layers.10.mlp.up_proj.weight', 'layers.10.post_attention_layernorm.weight', 'layers.10.self_attn.k_proj.weight', 'layers.10.self_attn.o_proj.weight', 'layers.10.self_attn.q_proj.weight', 'layers.10.self_attn.v_proj.weight', 'layers.11.input_layernorm.weight', 'layers.11.mlp.down_proj.weight', 'layers.11.mlp.gate_proj.weight', 'layers.11.mlp.up_proj.weight', 'layers.11.post_attention_layernorm.weight', 'layers.11.self_attn.k_proj.weight', 'layers.11.self_attn.o_proj.weight', 'layers.11.self_attn.q_proj.weight', 'layers.11.self_attn.v_proj.weight', 'layers.12.input_layernorm.weight', 'layers.12.mlp.down_proj.weight', 'layers.12.mlp.gate_proj.weight', 'layers.12.mlp.up_proj.weight', 'layers.12.post_attention_layernorm.weight', 'layers.12.self_attn.k_proj.weight', 'layers.12.self_attn.o_proj.weight', 'layers.12.self_attn.q_proj.weight', 'layers.12.self_attn.v_proj.weight', 'layers.13.input_layernorm.weight', 'layers.13.mlp.down_proj.weight', 'layers.13.mlp.gate_proj.weight', 'layers.13.mlp.up_proj.weight', 'layers.13.post_attention_layernorm.weight', 'layers.13.self_attn.k_proj.weight', 'layers.13.self_attn.o_proj.weight', 'layers.13.self_attn.q_proj.weight', 'layers.13.self_attn.v_proj.weight', 'layers.14.input_layernorm.weight', 'layers.14.mlp.down_proj.weight', 'layers.14.mlp.gate_proj.weight', 'layers.14.mlp.up_proj.weight', 'layers.14.post_attention_layernorm.weight', 'layers.14.self_attn.k_proj.weight', 'layers.14.self_attn.o_proj.weight', 'layers.14.self_attn.q_proj.weight', 'layers.14.self_attn.v_proj.weight', 'layers.15.input_layernorm.weight', 'layers.15.mlp.down_proj.weight', 'layers.15.mlp.gate_proj.weight', 'layers.15.mlp.up_proj.weight', 'layers.15.post_attention_layernorm.weight', 'layers.15.self_attn.k_proj.weight', 'layers.15.self_attn.o_proj.weight', 'layers.15.self_attn.q_proj.weight', 'layers.15.self_attn.v_proj.weight', 'layers.16.input_layernorm.weight', 'layers.16.mlp.down_proj.weight', 'layers.16.mlp.gate_proj.weight', 'layers.16.mlp.up_proj.weight', 'layers.16.post_attention_layernorm.weight', 'layers.16.self_attn.k_proj.weight', 'layers.16.self_attn.o_proj.weight', 'layers.16.self_attn.q_proj.weight', 'layers.16.self_attn.v_proj.weight', 'layers.17.input_layernorm.weight', 'layers.17.mlp.down_proj.weight', 'layers.17.mlp.gate_proj.weight', 'layers.17.mlp.up_proj.weight', 'layers.17.post_attention_layernorm.weight', 'layers.17.self_attn.k_proj.weight', 'layers.17.self_attn.o_proj.weight', 'layers.17.self_attn.q_proj.weight', 'layers.17.self_attn.v_proj.weight', 'layers.18.input_layernorm.weight', 'layers.18.mlp.down_proj.weight', 'layers.18.mlp.gate_proj.weight', 'layers.18.mlp.up_proj.weight', 'layers.18.post_attention_layernorm.weight', 'layers.18.self_attn.k_proj.weight', 'layers.18.self_attn.o_proj.weight', 'layers.18.self_attn.q_proj.weight', 'layers.18.self_attn.v_proj.weight', 'layers.19.input_layernorm.weight', 'layers.19.mlp.down_proj.weight', 'layers.19.mlp.gate_proj.weight', 'layers.19.mlp.up_proj.weight', 'layers.19.post_attention_layernorm.weight', 'layers.19.self_attn.k_proj.weight', 'layers.19.self_attn.o_proj.weight', 'layers.19.self_attn.q_proj.weight', 'layers.19.self_attn.v_proj.weight', 'layers.2.input_layernorm.weight', 'layers.2.mlp.down_proj.weight', 'layers.2.mlp.gate_proj.weight', 'layers.2.mlp.up_proj.weight', 'layers.2.post_attention_layernorm.weight', 'layers.2.self_attn.k_proj.weight', 'layers.2.self_attn.o_proj.weight', 'layers.2.self_attn.q_proj.weight', 'layers.2.self_attn.v_proj.weight', 'layers.20.input_layernorm.weight', 'layers.20.mlp.down_proj.weight', 'layers.20.mlp.gate_proj.weight', 'layers.20.mlp.up_proj.weight', 'layers.20.post_attention_layernorm.weight', 'layers.20.self_attn.k_proj.weight', 'layers.20.self_attn.o_proj.weight', 'layers.20.self_attn.q_proj.weight', 'layers.20.self_attn.v_proj.weight', 'layers.21.input_layernorm.weight', 'layers.21.mlp.down_proj.weight', 'layers.21.mlp.gate_proj.weight', 'layers.21.mlp.up_proj.weight', 'layers.21.post_attention_layernorm.weight', 'layers.21.self_attn.k_proj.weight', 'layers.21.self_attn.o_proj.weight', 'layers.21.self_attn.q_proj.weight', 'layers.21.self_attn.v_proj.weight', 'layers.3.input_layernorm.weight', 'layers.3.mlp.down_proj.weight', 'layers.3.mlp.gate_proj.weight', 'layers.3.mlp.up_proj.weight', 'layers.3.post_attention_layernorm.weight', 'layers.3.self_attn.k_proj.weight', 'layers.3.self_attn.o_proj.weight', 'layers.3.self_attn.q_proj.weight', 'layers.3.self_attn.v_proj.weight', 'layers.4.input_layernorm.weight', 'layers.4.mlp.down_proj.weight', 'layers.4.mlp.gate_proj.weight', 'layers.4.mlp.up_proj.weight', 'layers.4.post_attention_layernorm.weight', 'layers.4.self_attn.k_proj.weight', 'layers.4.self_attn.o_proj.weight', 'layers.4.self_attn.q_proj.weight', 'layers.4.self_attn.v_proj.weight', 'layers.5.input_layernorm.weight', 'layers.5.mlp.down_proj.weight', 'layers.5.mlp.gate_proj.weight', 'layers.5.mlp.up_proj.weight', 'layers.5.post_attention_layernorm.weight', 'layers.5.self_attn.k_proj.weight', 'layers.5.self_attn.o_proj.weight', 'layers.5.self_attn.q_proj.weight', 'layers.5.self_attn.v_proj.weight', 'layers.6.input_layernorm.weight', 'layers.6.mlp.down_proj.weight', 'layers.6.mlp.gate_proj.weight', 'layers.6.mlp.up_proj.weight', 'layers.6.post_attention_layernorm.weight', 'layers.6.self_attn.k_proj.weight', 'layers.6.self_attn.o_proj.weight', 'layers.6.self_attn.q_proj.weight', 'layers.6.self_attn.v_proj.weight', 'layers.7.input_layernorm.weight', 'layers.7.mlp.down_proj.weight', 'layers.7.mlp.gate_proj.weight', 'layers.7.mlp.up_proj.weight', 'layers.7.post_attention_layernorm.weight', 'layers.7.self_attn.k_proj.weight', 'layers.7.self_attn.o_proj.weight', 'layers.7.self_attn.q_proj.weight', 'layers.7.self_attn.v_proj.weight', 'layers.8.input_layernorm.weight', 'layers.8.mlp.down_proj.weight', 'layers.8.mlp.gate_proj.weight', 'layers.8.mlp.up_proj.weight', 'layers.8.post_attention_layernorm.weight', 'layers.8.self_attn.k_proj.weight', 'layers.8.self_attn.o_proj.weight', 'layers.8.self_attn.q_proj.weight', 'layers.8.self_attn.v_proj.weight', 'layers.9.input_layernorm.weight', 'layers.9.mlp.down_proj.weight', 'layers.9.mlp.gate_proj.weight', 'layers.9.mlp.up_proj.weight', 'layers.9.post_attention_layernorm.weight', 'layers.9.self_attn.k_proj.weight', 'layers.9.self_attn.o_proj.weight', 'layers.9.self_attn.q_proj.weight', 'layers.9.self_attn.v_proj.weight', 'norm.weight', 'qa_outputs.bias', 'qa_outputs.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
  0%|          | 0/21118 [00:00<?, ?it/s]Traceback (most recent call last):
  File "C:\Users\Levente\Desktop\minigpt\train.py", line 136, in <module>
    trainer.train()
  File "C:\Users\Levente\Desktop\minigpt\.venv\Lib\site-packages\transformers\trainer.py", line 1859, in train
    return inner_training_loop(
           ^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Levente\Desktop\minigpt\.venv\Lib\site-packages\transformers\trainer.py", line 2203, in _inner_training_loop
    tr_loss_step = self.training_step(model, inputs)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Levente\Desktop\minigpt\.venv\Lib\site-packages\transformers\trainer.py", line 3138, in training_step
    loss = self.compute_loss(model, inputs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Levente\Desktop\minigpt\.venv\Lib\site-packages\transformers\trainer.py", line 3179, in compute_loss
    raise ValueError(
ValueError: The model did not return a loss from the inputs, only the following keys: start_logits,end_logits. For reference, the inputs it received are input_ids,attention_mask.

Process finished with exit code 1What could be the solution to the problem to make the program run successfully? Unfortunately, time is pressing, and I’m quite puzzled.Unfortunately, I’ve tried many things, but I’ve encountered various other problems. The data processing works fine, but the issue always arises when I pass it to the trainer. There was a problem with passing a list containing dictionaries, and another problem occurred with “Scalar tensor has no len()”. What could be the solution? Could you help me fix the code?
2024-05-05T20:01:53Z
Have you tried the LlamaForCausalLM, LlamaTokenizer, or AutoModelForCausalLM,  AutoTokenizer?Also, sure if you need to pass the labels, as the model doesn’t shift the input_ids one position right to create the labels?
2024-05-06T04:41:31Z
I’ll try, but does the model also learn question-and-answer tasks this way?
2024-05-06T07:37:07Z
lev4922:ValueError: The model did not return a loss from the inputs, only the following keys: start_logits,end_logits. For reference, the inputs it received are input_ids,attention_mask.Hi,This error indicates that labels weren’t passed successfully to the model. Classes of typexxxForQuestionAnsweringrequire not thelabelskeyword argument, but ratherstart_positionsandend_positions. This is a bit different compared to models likexxxForSequenceClassificationandxxxForTokenClassificationwhich require thelabelskeyword argument.This is because models of typexxxForQuestionAnsweringare extractive question answering models which are trained to predict which token of the context is at the start and which token of the context is at the end of the answer. Hence during training, we need to indicate to the model which tokens are at the start of the answer (start_positions) and which tokens are at the end of the answer (end_positions).Refer to the official question answering notebook which explains all of that in more detail:notebooks/examples/question_answering.ipynb at main · huggingface/notebooks · GitHub. We also have official training scripts:transformers/examples/pytorch/question-answering at main · huggingface/transformers · GitHub.