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#!/usr/bin/env python
# coding: utf-8
from datasets import ClassLabel, Sequence
import random
import pandas as pd
from IPython.display import display, HTML
from codecarbon import OfflineEmissionsTracker
from experiment_impact_tracker.compute_tracker import ImpactTracker
import argparse
import shutil
import os
from datasets import load_dataset, load_metric
import transformers
from transformers import AutoTokenizer, AutoModelForTokenClassification, TrainingArguments, Trainer, DataCollatorForTokenClassification
import numpy as np
import tensorflow as tf
from sklearn.metrics import classification_report
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model",
default=None,
type=str,
required=True,
help="Model given through huggingface (e.g.: qwant/fralbert-base)",
)
parser.add_argument(
"--epoch",
default=10,
type=int,
required=False,
help="Number of epochs (10 by default)",
)
parser.add_argument(
"--to_train",
default=True,
type=bool,
required=False,
help="Launch training",
)
parser.add_argument(
"--to_eval",
default=True,
type=bool,
required=False,
help="Launch evaluation",
)
argparsed = parser.parse_args()
os.environ["WANDB_DISABLED"] = "true"
os.path.isdir("./")
datasets = load_dataset(
path = "media.py",
data_files={
"train":"train.txt",
"dev": "dev.txt",
"test": "test.txt"
}
)
label_list = datasets["train"].features[f"chunk_tags"].feature.names
def show_random_elements(dataset, num_examples=10):
assert num_examples <= len(dataset), "Can't pick more elements than there are in the dataset."
picks = []
for _ in range(num_examples):
pick = random.randint(0, len(dataset)-1)
while pick in picks:
pick = random.randint(0, len(dataset)-1)
picks.append(pick)
df = pd.DataFrame(dataset[picks])
for column, typ in dataset.features.items():
if isinstance(typ, ClassLabel):
df[column] = df[column].transform(lambda i: typ.names[i])
elif isinstance(typ, Sequence) and isinstance(typ.feature, ClassLabel):
df[column] = df[column].transform(lambda x: [typ.feature.names[i] for i in x])
display(HTML(df.to_html()))
show_random_elements(datasets["train"])
#model_checkpoint = "xlm-roberta-large" #ND
#model_checkpoint = "qwant/fralbert-base"
#model_checkpoint = "camembert/camembert-base-wikipedia-4gb"
model_checkpoint = argparsed.model
#qwant/fralbert-base
#xlm-roberta-base
#xlm-roberta-large
#Geotrend/bert-base-fr-cased
#distilbert-base-multilingual-cased
#albert-base-v2
#camembert-base
#camembert/camembert-large
#bert-base-multilingual-cased
task = "chunk" # Should be one of "ner", "pos" or "chunk"
batch_size = 8
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
assert isinstance(tokenizer, transformers.PreTrainedTokenizerFast)
label_all_tokens = True
def tokenize_and_align_labels(examples):
tokenized_inputs = tokenizer(examples["tokens"], truncation=True, is_split_into_words=True)
labels = []
for i, label in enumerate(examples[f"{task}_tags"]):
word_ids = tokenized_inputs.word_ids(batch_index=i)
previous_word_idx = None
label_ids = []
for word_idx in word_ids:
# Special tokens have a word id that is None. We set the label to -100 so they are automatically
# ignored in the loss function.
if word_idx is None:
label_ids.append(-100)
# We set the label for the first token of each word.
elif word_idx != previous_word_idx:
label_ids.append(label[word_idx])
# For the other tokens in a word, we set the label to either the current label or -100, depending on
# the label_all_tokens flag.
else:
label_ids.append(label[word_idx] if label_all_tokens else -100)
previous_word_idx = word_idx
labels.append(label_ids)
tokenized_inputs["labels"] = labels
return tokenized_inputs
tokenized_datasets = datasets.map(tokenize_and_align_labels, batched=True)
model = AutoModelForTokenClassification.from_pretrained(model_checkpoint, num_labels=len(label_list))
args = TrainingArguments(
f"models/media/"+"outputs_" + model_checkpoint.replace("/","_"),
evaluation_strategy = "epoch",
learning_rate=2e-5,
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
num_train_epochs=argparsed.epoch,
weight_decay=0.01,
)
data_collator = DataCollatorForTokenClassification(tokenizer)
metric = load_metric("seqeval")
#example = datasets["train"][22]
#example["tokens"]
#labels = [label_list[i] for i in example[f"{task}_tags"]]
#metric.compute(predictions=[labels], references=[labels])
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def compute_metrics(p):
predictions, labels = p
predictions = np.argmax(predictions, axis=2)
# Remove ignored index (special tokens)
true_predictions = [
[label_list[p] for (p, l) in zip(prediction, label) if l != -100]
for prediction, label in zip(predictions, labels)
]
true_labels = [
[label_list[l] for (p, l) in zip(prediction, label) if l != -100]
for prediction, label in zip(predictions, labels)
]
results = metric.compute(predictions=true_predictions, references=true_labels)
return {
"precision": results["overall_precision"],
"recall": results["overall_recall"],
"f1": results["overall_f1"],
"accuracy": results["overall_accuracy"],
}
def print_results(results):
strout = "Precision: " + str(round(100*results["overall_precision"],2))
strout = strout + "\nRecall: " + str(round(100*results["overall_recall"],2))
strout = strout + "\nF1: " + str(round(100*results["overall_f1"],2))
strout = strout + "\nAccuracy: " + str(round(100*results["overall_accuracy"],2))
return strout
args.metric_for_best_model="f1",
args.greater_is_better=True
trainer = Trainer(
model,
args,
train_dataset=tokenized_datasets["train"],
eval_dataset=tokenized_datasets["validation"],
data_collator=data_collator,
tokenizer=tokenizer,
compute_metrics=compute_metrics,
)
if not os.path.exists("outputs_" + model_checkpoint.replace("/","_") + "/ImpactTrackerTrain"):
os.makedirs("outputs_" + model_checkpoint.replace("/","_") + "/ImpactTrackerTrain")
if not os.path.exists("outputs_" + model_checkpoint.replace("/","_") + "/ImpactTrackerTest"):
os.makedirs("outputs_" + model_checkpoint.replace("/","_") + "/ImpactTrackerTest")
tracker = OfflineEmissionsTracker(country_iso_code="FRA")
#tracker_plop = ImpactTracker('.')
#tracker_plop.launch_impact_monitor()
tracker.start()
#print(tracker.gpu_ids)
#tracker.log_level = DEBUG
to_train = True
to_evaluate = True
if to_train:
impact_dir = "outputs_" + model_checkpoint.replace("/","_") + "/ImpactTrackerTrain"
myimpacttrackertrain = ImpactTracker(impact_dir)
myimpacttrackertrain.launch_impact_monitor()
trainer.train()
emissions: float = tracker.stop()
trainer.evaluate(eval_dataset=tokenized_datasets["validation"])
tracker_test = OfflineEmissionsTracker(country_iso_code="FRA")
tracker_test.start()
if to_evaluate:
impact_dir = "outputs_" + model_checkpoint.replace("/","_") + "/ImpactTrackerTest"
myimpacttrackerinference = ImpactTracker(impact_dir)
myimpacttrackerinference.launch_impact_monitor()
trainer.evaluate(eval_dataset=tokenized_datasets["test"])
predictions, labels, _ = trainer.predict(tokenized_datasets["test"])
predictions = np.argmax(predictions, axis=2)
# Remove ignored index (special tokens)
true_predictions = [
[label_list[p] for (p, l) in zip(prediction, label) if l != -100]
for prediction, label in zip(predictions, labels)
]
true_labels = [
[label_list[l] for (p, l) in zip(prediction, label) if l != -100]
for prediction, label in zip(predictions, labels)
]
#print(true_predictions)
#print(true_labels)
results = metric.compute(predictions=true_predictions, references=true_labels)
emissions: float = tracker_test.stop()
#print(classification_report(true_predictions, true_labels))
print(print_results(results))
shutil.move("emissions.csv","outputs_" + model_checkpoint.replace("/","_") + "/emissions.csv")