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import os
import numpy as np
import pandas as pd
import os
from tqdm import tqdm
from transformers import pipeline
from transformers import AutoTokenizer, FalconForCausalLM
import torch
from datasets import Dataset
from peft import LoraConfig
from trl import SFTTrainer
from transformers import (
AutoTokenizer,
BitsAndBytesConfig,
TrainingArguments,
pipeline,
)
from sklearn.metrics import (accuracy_score,
classification_report,
confusion_matrix)
from sklearn.model_selection import train_test_split
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
os.environ["TOKENIZERS_PARALLELISM"] = "false"
import warnings
warnings.filterwarnings("ignore")
def generate_prompt(data_point):
return f"""### Instruction:
Classify whether the given chunk involves a decision that will effect the story or not.
A decision is defined as when the character goes about making a choice between two or more options.
The decision should be significant enough to affect the story in a major way.
It doesn't really involve emotions, feelings or thoughts, but what the character does, or what happens to them.
This involes interactions between characters, or the character and the environment.
What isn't a decision is chunks describing the setting, or the character's thoughts or feelings.
Return the answer as the corresponding decision label "yes" or "no"
### Text:
{data_point["text"]}
### Decision:
{data_point["decision"]}
"""
def generate_test_prompt(data_point):
return f"""### Instruction:
Classify whether the given chunk involves a decision that will effect the story or not.
A decision is defined as when the character goes about making a choice between two or more options.
The decision should be significant enough to affect the story in a major way.
It doesn't really involve emotions, feelings or thoughts, but what the character does, or what happens to them.
This involes interactions between characters, or the character and the environment.
What isn't a decision is chunks describing the setting, or the character's thoughts or feelings.
Return the answer as the corresponding decision label "yes" or "no"
### Text:
{data_point["text"]}
### Decision:
"""
def predict(X_test, model, tokenizer):
y_pred = []
for i in tqdm(range(len(X_test))):
prompt = X_test.iloc[i]["text"]
pipe = pipeline(task="text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens = 1,
temperature = 0.0,
)
result = pipe(prompt, pad_token_id=pipe.tokenizer.eos_token_id)
answer = result[0]['generated_text'].split("=")[-1].lower()
if "yes" in answer:
y_pred.append("yes")
elif "no" in answer:
y_pred.append("no")
else:
y_pred.append("none")
return y_pred
def evaluate(y_true, y_pred):
labels = ['yes', 'no', 'none']
mapping = {"yes": 1, "no": 0, 'none':2}
def map_func(x):
return mapping.get(x, 1)
y_true = np.vectorize(map_func)(y_true)
y_pred = np.vectorize(map_func)(y_pred)
# Calculate accuracy
accuracy = accuracy_score(y_true=y_true, y_pred=y_pred)
print(f'Accuracy: {accuracy:.3f}')
# Generate accuracy report
unique_labels = set(y_true) # Get unique labels
for label in unique_labels:
label_indices = [i for i in range(len(y_true))
if y_true[i] == label]
label_y_true = [y_true[i] for i in label_indices]
label_y_pred = [y_pred[i] for i in label_indices]
accuracy = accuracy_score(label_y_true, label_y_pred)
print(f'Accuracy for label {label}: {accuracy:.3f}')
# Generate classification report
class_report = classification_report(y_true=y_true, y_pred=y_pred)
print('\nClassification Report:')
print(class_report)
# Generate confusion matrix
conf_matrix = confusion_matrix(y_true=y_true, y_pred=y_pred, labels=[0, 1, 2])
print('\nConfusion Matrix:')
print(conf_matrix)
def prep_data():
filename = '../../data/output/decisions.csv'
df = pd.read_csv(filename, encoding="utf-8", encoding_errors="replace")
df = df[['text', 'decision']]
X_train = list()
X_test = list()
for decision in ["yes", "no"]:
train, test = train_test_split(df[df.decision==decision],
train_size=.8,
test_size=.2,
random_state=42)
X_train.append(train)
X_test.append(test)
X_train = pd.concat(X_train).sample(frac=1, random_state=10)
X_test = pd.concat(X_test)
eval_idx = [idx for idx in df.index if idx not in list(train.index) + list(test.index)]
X_eval = df[df.index.isin(eval_idx)]
X_eval = (X_eval
.groupby('decision', group_keys=False)
.apply(lambda x: x.sample(n=50, random_state=10, replace=True)))
X_train = X_train.reset_index(drop=True)
X_train = pd.DataFrame(X_train.apply(generate_prompt, axis=1),
columns=["text"])
X_eval = pd.DataFrame(X_eval.apply(generate_prompt, axis=1),
columns=["text"])
y_true = X_test.decision
X_test = pd.DataFrame(X_test.apply(generate_test_prompt, axis=1), columns=["text"])
train_data = Dataset.from_pandas(X_train)
eval_data = Dataset.from_pandas(X_eval)
return train_data, eval_data
def prep_model():
model_name = "Rocketknight1/falcon-rw-1b"
compute_dtype = getattr(torch, "float16")
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=False,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=compute_dtype,
)
model = FalconForCausalLM.from_pretrained(
model_name,
device_map="auto",
quantization_config=bnb_config,
)
model.config.use_cache = False
model.config.pretraining_tp = 1
tokenizer = AutoTokenizer.from_pretrained(model_name,
trust_remote_code=True,
padding_side="left",
add_bos_token=True,
add_eos_token=True,
)
tokenizer.pad_token = tokenizer.eos_token
return model, tokenizer
def prep_trainer():
OUTPUT_DIR = "falcon-clf"
train_data, eval_data = prep_data()
model, tokenizer = prep_model()
peft_config = LoraConfig(
lora_alpha=16,
lora_dropout=0.1,
r=64,
bias="none",
task_type="CAUSAL_LM",
)
training_arguments = TrainingArguments(
output_dir=OUTPUT_DIR,
num_train_epochs=20,
per_device_train_batch_size=1,
gradient_accumulation_steps=8, # 4
optim="paged_adamw_32bit",
save_steps=0,
logging_steps=10,
learning_rate=2e-4,
weight_decay=0.001,
fp16=True,
bf16=False,
max_grad_norm=0.3,
max_steps=-1,
warmup_ratio=0.03,
group_by_length=True,
lr_scheduler_type="cosine",
report_to="tensorboard",
evaluation_strategy="epoch"
)
trainer = SFTTrainer(
model=model,
train_dataset=train_data,
eval_dataset=eval_data,
peft_config=peft_config,
dataset_text_field="text",
tokenizer=tokenizer,
args=training_arguments,
packing=False,
max_seq_length=1024,
)
return trainer
def train_model():
trainer = prep_trainer()
trainer.train()
trainer.model.save_pretrained("falcon-clf")
trainer.push_to_hub()
def get_classifier():
classifier = pipeline(model=f"suneeln-duke/falcon-clf", device_map="auto")
return classifier
def classify_dec(text, classifier):
text = generate_test_prompt({
'text': text
})
result = classifier(text, pad_token_id=classifier.tokenizer.eos_token_id)
answer = result[0]['generated_text'].split("=")[-1].lower()
if "yes" in answer:
return "yes"
elif "no" in answer:
return "no" |