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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from datasets import load_dataset
from transformers import AutoTokenizer
from datasets import Dataset, load_metric
from sklearn.model_selection import train_test_split
from source.services.predicting_effective_arguments.train.model import TransformersSequenceClassifier
class CFG:
TARGET = 'discourse_effectiveness'
TEXT = "discourse_text"
MODEL_CHECKPOINT = "distilbert-base-uncased"
MODEL_OUTPUT_DIR ='source/services/predicting_effective_arguments/model/hf_textclassification/predicting_effective_arguments_distilbert'
model_name="debertav3base"
learning_rate=1.5e-5
weight_decay=0.02
hidden_dropout_prob=0.007
attention_probs_dropout_prob=0.007
num_train_epochs=10
n_splits=4
batch_size=12
random_seed=42
save_steps=100
max_length=512
def seed_everything(seed: int):
import random, os
import numpy as np
import torch
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = True
def prepare_input_text(df, sep_token):
df['inputs'] = df.discourse_type.str.lower() + ' ' + sep_token + ' ' + df.discourse_text.str.lower()
return df
if __name__ == '__main__':
config = CFG()
tokenizer = AutoTokenizer.from_pretrained(config.MODEL_CHECKPOINT)
seqClassifer = TransformersSequenceClassifier(model_output_dir=config.MODEL_OUTPUT_DIR, tokenizer=tokenizer, model_checkpoint="distilbert-base-uncased", num_labels=3) #distilbert-base-uncased
data = pd.read_csv("data/raw_data/train.csv")[:100]
test_df = pd.read_csv("data/raw_data/test.csv")
train_size = 0.7
valid_size = 0.2
test_size = 0.1
# First split: Separate out the training set
train_df, temp_df = train_test_split(data, test_size=1 - train_size, random_state=5600)
# Second split: Separate out the validation and test sets
valid_df, test_df = train_test_split(temp_df, test_size=test_size / (test_size + valid_size), random_state=5600)
train_df = prepare_input_text(train_df, sep_token=tokenizer.sep_token)
valid_df = prepare_input_text(valid_df, sep_token=tokenizer.sep_token)
test_df = prepare_input_text(test_df, sep_token=tokenizer.sep_token)
train_dataset = Dataset.from_pandas(train_df[['inputs', config.TARGET]]).rename_column(config.TARGET, 'label').class_encode_column("label")
val_dataset = Dataset.from_pandas(valid_df[['inputs', config.TARGET]]).rename_column(config.TARGET, 'label').class_encode_column("label")
test_dataset = Dataset.from_pandas(test_df[['inputs', config.TARGET]]).rename_column(config.TARGET, 'label').class_encode_column("label")
labels = train_dataset.features["label"].names
train_tok_dataset = seqClassifer.tokenize_dataset(dataset=train_dataset)
val_tok_dataset = seqClassifer.tokenize_dataset(dataset=val_dataset)
test_tok_dataset = seqClassifer.tokenize_dataset(dataset=test_dataset)
seqClassifer.train(train_dataset=train_tok_dataset, eval_dataset=val_tok_dataset, epochs=1, batch_size=16)
y_valid_pred = seqClassifer.predict_valid_data(val_tok_dataset)
seqClassifer.plot_confusion_matrix(y_preds=y_valid_pred, y_true=val_dataset['label'], labels=labels)
y_test_pred = seqClassifer.predict_test_data(model_checkpoint=config.MODEL_OUTPUT_DIR, test_list=test_df['inputs'].tolist())
pass
"""
train_df[TARGET].value_counts(ascending=True).plot.barh()
plt.title("Frequency of Classes")
plt.show()
train_df['discourse_type'].value_counts(ascending=True).plot.barh()
plt.title("Frequency of discourse_type")
plt.show()
train_df["Words Per text"] = train_df[TEXT].str.split().apply(len)
train_df.boxplot("Words Per text", by=TARGET, grid=False, showfliers=False,
color="black")
plt.suptitle("")
plt.xlabel("")
plt.show()
""" |