I am generally following this tutorial (https://huggingface.co/docs/transformers/training#:~:text=%F0%9F%A4%97%20Transformers%20provides%20access%20to,an%20incredibly%20powerful%20training%20technique.) to implement fine-tuning on a pretrained transformer. The main difference is I am using my own custom dataset that is being sourced from a JSON file that has a document's text and the label it should belong to. To be able to do this I needed to create my own class which is based off of the Dataset class from pytorch. This is what that class looks like:
class PDFsDataset(torch.utils.data.Dataset):
def __init__(self, encodings, labels):
self.encodings = encodings
self.labels = labels
def __getitem__(self, idx):
print("\n\n\n\nindex",idx)
item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
item['labels'] = torch.tensor(self.labels[idx])
return item
def __len__(self):
return len(self.labels)
I am getting an error when training the transformer that says
Traceback (most recent call last):
File "C:\Users\e417922\AppData\Roaming\Python\Python39\site-packages\pandas\core\indexes\base.py", line 3621, in get_loc
return self._engine.get_loc(casted_key)
File "pandas\_libs\index.pyx", line 136, in pandas._libs.index.IndexEngine.get_loc
File "pandas\_libs\index.pyx", line 163, in pandas._libs.index.IndexEngine.get_loc
File "pandas\_libs\hashtable_class_helper.pxi", line 2131, in pandas._libs.hashtable.Int64HashTable.get_item
File "pandas\_libs\hashtable_class_helper.pxi", line 2140, in pandas._libs.hashtable.Int64HashTable.get_item
KeyError: 19
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "c:\Users\e417922\Downloads\enwiki-20220601-pages-meta-history1.xml-p1p857\HF_Transformer.py", line 147, in <module>
transformer.train_transformer()
File "c:\Users\e417922\Downloads\enwiki-20220601-pages-meta-history1.xml-p1p857\HF_Transformer.py", line 135, in train_transformer
trainer.train()
File "C:\Users\e417922\AppData\Roaming\Python\Python39\site-packages\transformers\trainer.py", line 1409, in train
return inner_training_loop(
File "C:\Users\e417922\AppData\Roaming\Python\Python39\site-packages\transformers\trainer.py", line 1625, in _inner_training_loop
for step, inputs in enumerate(epoch_iterator):
File "C:\Users\e417922\AppData\Roaming\Python\Python39\site-packages\torch\utils\data\dataloader.py", line 530, in __next__
data = self._next_data()
File "C:\Users\e417922\AppData\Roaming\Python\Python39\site-packages\torch\utils\data\dataloader.py", line 570, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "C:\Users\e417922\AppData\Roaming\Python\Python39\site-packages\torch\utils\data\_utils\fetch.py", line 49, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "C:\Users\e417922\AppData\Roaming\Python\Python39\site-packages\torch\utils\data\_utils\fetch.py", line 49, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "c:\Users\e417922\Downloads\enwiki-20220601-pages-meta-history1.xml-p1p857\HF_Transformer.py", line 42, in __getitem__
for key in self.encodings[idx]:
File "C:\Users\e417922\AppData\Roaming\Python\Python39\site-packages\pandas\core\series.py", line 958, in __getitem__
return self._get_value(key)
File "C:\Users\e417922\AppData\Roaming\Python\Python39\site-packages\pandas\core\series.py", line 1069, in _get_value
loc = self.index.get_loc(label)
File "C:\Users\e417922\AppData\Roaming\Python\Python39\site-packages\pandas\core\indexes\base.py", line 3623, in get_loc
raise KeyError(key) from err
KeyError: 19
The KeyError that it fails on changes each time I run it. I'm a beginner with Transformers and HuggingFace so I have no clue what's causing this problem.
Edit: Sample Input is a JSON File where elements would look like this: { "text_clean": [ "article with a few hundred words", another article with a lot of words", "yet another article" ], "most_similar_label":[ "Quantum" "Artificial intelligence" "Materials" ] }
Full Code:
import tkinter as tk
from tkinter import filedialog
import json
import pandas as pd
from transformers import AutoTokenizer
from transformers import AutoModelForSequenceClassification
from transformers import TrainingArguments
from transformers import TrainingArguments, Trainer
import numpy as np
from datasets import load_metric
from sklearn.model_selection import train_test_split
import torch
class PDFsDataset(torch.utils.data.Dataset):
def __init__(self, encodings, labels):
self.encodings = encodings
self.labels = labels
def __getitem__(self, idx):
print("\n\n\n\nindex",idx)
item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
item['labels'] = torch.tensor(self.labels[idx])
return item
def __len__(self):
return len(self.labels)
class HFTransformer:
def __init__ (self):
pass
def import_from_json(self):
#Prompts user to select json file
root = tk.Tk()
root.withdraw()
self.json_file_path = filedialog.askopenfile().name
#opens json file and loads data
with open(self.json_file_path, "r") as json_file:
try:
json_load = json.load(json_file)
except:
raise ValueError("No PDFs to convert to JSON")
self.pdfs = json_load
#converts json file data to dataframe for easier manipulation
self.pdfs = pd.DataFrame.from_dict(self.pdfs)
for index in range(len(self.pdfs["new_tags"])):
if self.pdfs["new_tags"][index] == "":
self.pdfs["new_tags"][index] = self.pdfs["most_similar_label"][index]
self.pdfs["labels"] = self.pdfs["new_tags"].apply(lambda val: self.change_tag_to_num(val))
# for label in self.data["labels"]:
def change_tag_to_num(self, value):
if value == "Quantum":
return 0
elif value == "Artificial intelligence":
return 1
elif value == "Materials":
return 2
elif value == "Energy":
return 3
elif value == "Defense":
return 4
elif value == "Satellite":
return 5
elif value == "Other":
return 6
def tokenize_dataset(self):
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
X_train, X_test, y_train, y_test = train_test_split(self.pdfs["text_clean"], self.pdfs["labels"],test_size=0.2)
train_encodings = X_train.apply(lambda string: tokenizer(string, truncation=True, padding=True,max_length=10))
test_encodings = X_test.apply(lambda string: tokenizer(string, truncation=True, padding=True,max_length=10))
self.train_dataset = PDFsDataset(train_encodings, y_train)
data_to_add = {"input_ids": [], "token_type_ids": [], "attention_mask": []}
for i in self.train_dataset.encodings:
data_to_add["input_ids"].append(i["input_ids"])
data_to_add["token_type_ids"].append(i["token_type_ids"])
data_to_add["attention_mask"].append(i["attention_mask"])
self.train_dataset.encodings = data_to_add
self.eval_dataset = PDFsDataset(test_encodings,y_test)
data_to_add = {"input_ids": [], "token_type_ids": [], "attention_mask": []}
for i in self.eval_dataset.encodings:
data_to_add["input_ids"].append(i["input_ids"])
data_to_add["token_type_ids"].append(i["token_type_ids"])
data_to_add["attention_mask"].append(i["attention_mask"])
self.eval_dataset.encodings = data_to_add
def train_transformer(self):
model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=7)
training_args = TrainingArguments(output_dir="test_trainer")
self.metric = load_metric("accuracy")
training_args = TrainingArguments(output_dir="test_trainer", evaluation_strategy="epoch")
trainer = Trainer(
model=model,
args=training_args,
train_dataset=self.train_dataset,
eval_dataset=self.eval_dataset,
compute_metrics=self.compute_metrics
)
trainer.train()
def compute_metrics(self, eval_pred):
logits, labels = eval_pred
predictions = np.argmax(logits, axis=-1)
return self.metric.compute(predictions=predictions, references=labels)
if __name__ == "__main__":
transformer = HFTransformer()
transformer.import_from_json()
transformer.tokenize_dataset()
transformer.train_transformer()
Converting pandas.Series into a simple python list and getting rid of some extra materials would fix the issue
class PDFsDataset(torch.utils.data.Dataset):
def __init__(self, encodings, labels):
self.encodings = encodings
self.labels = labels
def __getitem__(self, idx):
item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
item['labels'] = torch.tensor(self.labels[idx])
return item
def __len__(self):
return len(self.labels)
class HFTransformer:
def __init__ (self):
pass
def import_from_json(self):
#Prompts user to select json file
self.json_file_path = '/content/truncated_data.json'
#opens json file and loads data
with open(self.json_file_path, "r") as json_file:
try:
json_load = json.load(json_file)
except:
raise ValueError("No PDFs to convert to JSON")
self.pdfs = json_load
#converts json file data to dataframe for easier manipulation
self.pdfs = pd.DataFrame.from_dict(self.pdfs)
for index in range(len(self.pdfs["new_tags"])):
if self.pdfs["new_tags"][index] == "":
self.pdfs["new_tags"][index] = self.pdfs["most_similar_label"][index]
self.pdfs["labels"] = self.pdfs["new_tags"].apply(lambda val: self.change_tag_to_num(val))
# for label in self.data["labels"]:
def change_tag_to_num(self, value):
if value == "Quantum":
return 0
elif value == "Artificial intelligence":
return 1
elif value == "Materials":
return 2
elif value == "Energy":
return 3
elif value == "Defense":
return 4
elif value == "Satellite":
return 5
elif value == "Other":
return 6
def tokenize_dataset(self):
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
X_train, X_test, y_train, y_test = train_test_split(self.pdfs["text_clean"].to_list(), self.pdfs["labels"].to_list(),test_size=0.2)
train_encodings = tokenizer(X_train, truncation=True, padding=True,max_length=100)
test_encodings = tokenizer(X_test, truncation=True, padding=True,max_length=100)
self.train_dataset = PDFsDataset(train_encodings, y_train)
self.eval_dataset = PDFsDataset(test_encodings,y_test)
def train_transformer(self):
model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=7)
training_args = TrainingArguments(output_dir="test_trainer")
self.metric = load_metric("accuracy")
training_args = TrainingArguments(output_dir="test_trainer", evaluation_strategy="epoch", )
trainer = Trainer(
model=model,
args=training_args,
train_dataset=self.train_dataset,
eval_dataset=self.eval_dataset,
compute_metrics=self.compute_metrics
)
trainer.train()
def compute_metrics(self, eval_pred):
logits, labels = eval_pred
predictions = np.argmax(logits, axis=-1)
return self.metric.compute(predictions=predictions, references=labels)
if __name__ == "__main__":
tr = HFTransformer()
tr.import_from_json()
tr.tokenize_dataset()
tr.train_transformer()