The primary (and most necessary) step of any fine-tuning course of is information assortment. Right here, I extracted title-thumbnail pairs from my channel in a 2-step course of.
First, I used YouTube’s search API to extract the video IDs for all of the movies on my channel. Second, I used YouTube’s video API to extract the title and thumbnail URL of every of my long-form movies (i.e. longer than 3 min).
# imports
from top_secret import my_key
import requests
from isodate import parse_durationimport pandas as pd
import numpy as np
from sentence_transformers import SentenceTransformer
from datasets import DatasetDict, Dataset
channel_id = 'UCa9gErQ9AE5jT2DZLjXBIdA' # my YouTube channel ID
page_token = None # initialize web page token
url = 'https://www.googleapis.com/youtube/v3/search' # YouTube search API # extract video information throughout a number of search consequence pages
video_id_list = []
whereas page_token != 0:
params = {
"key": my_key,
'channelId': channel_id,
'half': ["snippet","id"],
'order': "date",
'maxResults':50,
'pageToken': page_token
}
response = requests.get(url, params=params)
for raw_item in dict(response.json())['items']:
# solely execute for youtube movies
if raw_item['id']['kind'] != "youtube#video":
proceed
# seize video ids
video_id_list.append(raw_item['id']['videoId'])
attempt:
# seize subsequent web page token
page_token = dict(response.json())['nextPageToken']
besides:
# if no subsequent web page token kill whereas loop
page_token = 0
Word that you’ll want a YouTube API key to run the above Python code, which you’ll be able to create utilizing the Google Cloud Console. To adapt this to your channel, you simply want to vary the channel_id variable.
# extract video titles and thumbnails
url = "https://www.googleapis.com/youtube/v3/movies"
video_data_list = []for video_id in video_id_list:
params = {
"half": ["snippet","contentDetails"],
"id": video_id,
"key": my_key,
}
response = requests.get(url, params=params)
raw_dict = dict(response.json())['items'][0]
# solely course of movies longer than 3 minutes
iso_duration = raw_dict['contentDetails']["duration"]
if parse_duration(iso_duration).total_seconds() proceed
# extract video information
video_data = {}
video_data['video_id'] = video_id
video_data['title'] = raw_dict['snippet']['title']
video_data['thumbnail_url'] = raw_dict['snippet']['thumbnails']['high']['url']
# append information to record
video_data_list.append(video_data)
As an extra step, I created detrimental thumbnail-title pairs. We will use these throughout the coaching course of to not solely information the mannequin with examples of which embedding ought to be shut collectively (i.e. optimistic pair), but in addition which embedding ought to be far aside (i.e. detrimental pairs).
To do that, I computed the similarity between all potential title pairs utilizing the sentence transformer library. Then for every optimistic pair, I matched the least comparable title as a detrimental instance (making certain there have been no duplicates).
# retailer information in dataframe
df = pd.DataFrame(video_data_list)# Load the mannequin
mannequin = SentenceTransformer("all-mpnet-base-v2")
# Encode all titles
embeddings = mannequin.encode(df['title'].to_list())
# compute similarities
similarities = mannequin.similarity(embeddings, embeddings)
# match least JDs least much like optimistic match because the detrimental match
similarities_argsorted = np.argsort(similarities.numpy(), axis=1)
negative_pair_index_list = []
for i in vary(len(similarities)):
# Begin with the smallest similarity index for the present row
j = 0
index = int(similarities_argsorted[i][j])
# Make sure the index is exclusive
whereas index in negative_pair_index_list:
j += 1 # Transfer to the following smallest index
index = int(similarities_argsorted[i][j]) # Fetch subsequent smallest index
negative_pair_index_list.append(index)
# add detrimental pairs to df
df['title_neg'] = df['title'].iloc[negative_pair_index_list].values
Lastly, I created a train-valid-test break up and pushed the dataset to the Hugging Face Hub.
# Shuffle the dataset
df = df.pattern(frac=1, random_state=42).reset_index(drop=True)# Break up into practice, validation, and check units
train_frac = 0.7
valid_frac = 0.15
test_frac = 0.15
# outline practice and validation measurement
train_size = int(train_frac * len(df))
valid_size = int(valid_frac * len(df))
# create practice, validation, and check datasets
df_train = df[:train_size]
df_valid = df[train_size:train_size + valid_size]
df_test = df[train_size + valid_size:]
# Convert the pandas DataFrames again to Hugging Face Datasets
train_ds = Dataset.from_pandas(df_train)
valid_ds = Dataset.from_pandas(df_valid)
test_ds = Dataset.from_pandas(df_test)
# Mix right into a DatasetDict
dataset_dict = DatasetDict({
'practice': train_ds,
'legitimate': valid_ds,
'check': test_ds
})
# push information to hub
dataset_dict.push_to_hub("shawhin/yt-title-thumbnail-pairs")
Though now we have all the info we’d like for fine-tuning, it’s nonetheless not an acceptable format for coaching. Extra particularly, we have to convert our picture URLs to PIL picture objects and set up our information into (anchor, optimistic, detrimental) triplets, i.e., a thumbnail, its corresponding title, and detrimental title, respectively.
We will course of all three information splits (i.e. practice, legitimate, and check) within the following means utilizing the Hugging Face Datasets library.
from PIL import Picture# load dataset
dataset = load_dataset("shawhin/yt-title-thumbnail-pairs")
# outline preprocessing perform
def preprocess(batch):
"""
Preprocessing information with out augmentations for check set
"""
# get photographs from urls
image_list = [Image.open(requests.get(url, stream=True).raw)
for url in batch["thumbnail_url"]]
# return columns with normal names
return {
"anchor": image_list,
"optimistic": batch["title"],
"detrimental": batch["title_neg"]
}
# take away columns not related to coaching
columns_to_remove = [col for col in dataset['train'].column_names
if col not in ['anchor', 'positive', 'negative']]
# apply transformations
dataset = dataset.map(preprocess, batched=True,
remove_columns=columns_to_remove)
It’s necessary that we order our columns as (anchor, optimistic, detrimental) triplets as a result of that is the format anticipated by the loss perform we’ll use throughout coaching (which I realized the exhausting means).
Coaching entails optimizing a mannequin’s parameters to attenuate a loss perform. Nevertheless, this worth (i.e. a contrastive loss) is never useful in assessing the mannequin’s efficiency on a downstream job (e.g. matching titles to thumbnails).
A amount that’s extra insightful, on this case, is the mannequin’s capability to appropriately match a given thumbnail to the proper title amongst a number of candidates. That is denoted Recall@1.
We will implement an evaluator suitable with the Sentence Transformers library to compute this metric. For the reason that code is kind of lengthy, I received’t paste it right here, however the curious reader can discover it in Cell 12 of this notebook.
# perform to create new evaluator given information break up
def create_recall_evaluator(set_name, okay=1):
"""
Create triplet evaluator for "practice", "legitimate", or "check" break up
"""return ImageTextRetrievalEvaluator(
photographs=dataset[f"{set_name}"]["anchor"],
texts=dataset[f"{set_name}"]["positive"],
identify=f"yt-title-thumbnail-{set_name}",
okay=okay
)
# Create new evaluator with Recall@okay
evaluator_recall_train = create_recall_evaluator("practice", okay=1)
evaluator_recall_valid = create_recall_evaluator("legitimate", okay=1)
print("Practice:", evaluator_recall_train(mannequin))
print("Legitimate:", evaluator_recall_valid(mannequin))
# >> Practice: {'yt-title-thumbnail-train_Recall@1': 0.660377358490566}
# >> Legitimate: {'yt-title-thumbnail-valid_Recall@1': 0.6363636363636364}
We will see the mannequin already has respectable efficiency out-of-the-box, with right titles being matched 66% of the time.
There are 3 key issues we should do earlier than coaching the mannequin. Specifically, select which parameters to coach, choose a loss perform, and set hyperparameters.
Trainable Parameters
The important thing limitation of this mission is that I’ve solely posted 76 YouTube movies (as of penning this). With the validation and check splits, this leaves solely 53 examples for coaching.
Since now we have so few coaching examples, limiting the variety of parameters we practice is a good suggestion. On this case, I solely practice the ultimate projection layer of the mannequin, which maps the textual content and picture embeddings right into a shared vector house. That is about 1M parameters complete.
# import mannequin
from sentence_transformers import SentenceTransformer
mannequin = SentenceTransformer("sentence-transformers/clip-ViT-L-14")# choose particular layers to coach (observe: you may add extra layers to this record)
trainable_layers_list = ['projection']
# Apply freezing configuration
for identify, param in mannequin.named_parameters():
# freeze all params
param.requires_grad = False
# unfreeze layers in trainable_layers_list
if any(layer in identify for layer in trainable_layers_list):
param.requires_grad = True
# Depend complete and trainable parameters
total_params = sum(p.numel() for p in mannequin.parameters())
trainable_params = sum(p.numel() for p in mannequin.parameters() if p.requires_grad)print(f"Whole parameters: {total_params:,}")
print(f"Trainable parameters: {trainable_params:,}")
print(f"% of trainable parameters: {100*trainable_params/total_params:.2f}%")
# >> Whole parameters: 427,616,513
# >> Trainable parameters: 1,376,256
# >> % of trainable parameters: 0.32%
Loss perform
Right here, I exploit the Multiple Negatives Ranking Loss from the Sentence Transformers library (which works with single negatives like on this case). It really works by maximizing the similarity between optimistic pairs whereas minimizing the similarity between detrimental pairs. Right here’s what the loss perform appears like for the only detrimental case [2].
from sentence_transformers.losses import MultipleNegativesRankingLoss# outline loss
loss = MultipleNegativesRankingLoss(mannequin)
Hyperparameters
For hyperparameters, I experimented with a handful of decisions manually and picked the selection with the very best validation loss and Recall@1 efficiency. Listed below are the ultimate decisions.
from sentence_transformers import SentenceTransformerTrainingArguments# hyperparameters
num_epochs = 2
batch_size = 16
lr = 1e-4
finetuned_model_name = "clip-title-thumbnail-embeddings"
train_args = SentenceTransformerTrainingArguments(
output_dir=f"fashions/{finetuned_model_name}",
num_train_epochs=num_epochs,
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
learning_rate=lr,
# Analysis settings
eval_strategy="epoch",
eval_steps=1,
logging_steps=1,
)
With our loss and hyperparameters outlined, we will practice the mannequin utilizing the SentenceTransformersTrainer().
from sentence_transformers import SentenceTransformerTrainercoach = SentenceTransformerTrainer(
mannequin=mannequin,
args=train_args,
train_dataset=dataset["train"],
eval_dataset=dataset["valid"],
loss=loss,
evaluator=[evaluator_recall_train, evaluator_recall_valid],
)
coach.practice()
Mannequin coaching is an iterative course of the place you might discover dozens of fashions for various decisions of trainable parameters, loss capabilities, and hyperparameters.
Nevertheless, I extremely suggest holding these experiments so simple as potential. If you end up spending an excessive amount of time tweaking coaching args to get your mannequin to converge, there’s in all probability one thing essentially incorrect together with your information (talking from expertise 😅).
As a ultimate step, we will consider the mannequin’s Recall@1 rating on the testing set. These information weren’t used for coaching or hyperparameter tuning, so it provides us an unbiased evaluation of the mannequin.
evaluator_recall_test = create_recall_evaluator("check")print("Practice:", evaluator_recall_train(mannequin))
print("Legitimate:", evaluator_recall_valid(mannequin))
print("Take a look at:", evaluator_recall_test(mannequin))
# >> Practice: {'yt-title-thumbnail-train_Recall@1': 0.8490566037735849}
# >> Legitimate: {'yt-title-thumbnail-valid_Recall@1': 0.9090909090909091}
# >> Take a look at: {'yt-title-thumbnail-test_Recall@1': 0.75}
We see that the mannequin performs properly throughout all three datasets with 75% Recall@1 on the check set. In different phrases, 75% of the time, the mannequin appropriately matches a given thumbnail to its unique title. Moreover, the recall for the validation dataset will increase by 27%!
Multimodal embedding fashions, like CLIP, unlock numerous 0-shot use circumstances similar to picture classification and retrieval. Right here, we noticed how we will fine-tune such a mannequin to adapt it to a specialised area (i.e. my YouTube titles and thumbnails).
Though CLIP is a small mannequin by immediately’s requirements (~500M parameters) and our coaching dataset was tiny, the ultimate mannequin nonetheless demonstrated sturdy efficiency on this job. This highlights the facility of fine-tuning.
If in case you have any questions or strategies for future content material, let me know within the feedback 🙂
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