The primary (and most vital) step of any fine-tuning course of is knowledge 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 knowledge throughout a number of search outcome 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'])
strive:
# 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 alter 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() < 180:
proceed
# extract video knowledge
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 knowledge to record
video_data_list.append(video_data)
As a further step, I created destructive thumbnail-title pairs. We are able to use these in the course of the coaching course of to not solely information the mannequin with examples of which embedding must be shut collectively (i.e. optimistic pair), but additionally which embedding must be far aside (i.e. destructive pairs).
To do that, I computed the similarity between all doable title pairs utilizing the sentence transformer library. Then for every optimistic pair, I matched the least comparable title as a destructive instance (making certain there have been no duplicates).
# retailer knowledge 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 destructive 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 subsequent smallest index
index = int(similarities_argsorted[i][j]) # Fetch subsequent smallest index
negative_pair_index_list.append(index)
# add destructive 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)# Cut up into prepare, validation, and check units
train_frac = 0.7
valid_frac = 0.15
test_frac = 0.15
# outline prepare and validation dimension
train_size = int(train_frac * len(df))
valid_size = int(valid_frac * len(df))
# create prepare, 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({
'prepare': train_ds,
'legitimate': valid_ds,
'check': test_ds
})
# push knowledge to hub
dataset_dict.push_to_hub("shawhin/yt-title-thumbnail-pairs")
Though we’ve all the info we want 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 manage our knowledge into (anchor, optimistic, destructive) triplets, i.e., a thumbnail, its corresponding title, and destructive title, respectively.
We are able to course of all three knowledge splits (i.e. prepare, legitimate, and check) within the following manner utilizing the Hugging Face Datasets library.
from PIL import Picture# load dataset
dataset = load_dataset("shawhin/yt-title-thumbnail-pairs")
# outline preprocessing operate
def preprocess(batch):
"""
Preprocessing knowledge with out augmentations for check set
"""
# get pictures from urls
image_list = [Image.open(requests.get(url, stream=True).raw)
for url in batch["thumbnail_url"]]
# return columns with customary names
return {
"anchor": image_list,
"optimistic": batch["title"],
"destructive": 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 vital that we order our columns as (anchor, optimistic, destructive) triplets as a result of that is the format anticipated by the loss operate we are going to use throughout coaching (which I realized the onerous manner).
Coaching entails optimizing a mannequin’s parameters to attenuate a loss operate. 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 are able to implement an evaluator appropriate with the Sentence Transformers library to compute this metric. Because the code is kind of lengthy, I received’t paste it right here, however the curious reader can discover it in Cell 12 of this pocket book.
# operate to create new evaluator given knowledge break up
def create_recall_evaluator(set_name, okay=1):
"""
Create triplet evaluator for "prepare", "legitimate", or "check" break up
"""return ImageTextRetrievalEvaluator(
pictures=dataset[f"{set_name}"]["anchor"],
texts=dataset[f"{set_name}"]["positive"],
title=f"yt-title-thumbnail-{set_name}",
okay=okay
)
# Create new evaluator with Recall@okay
evaluator_recall_train = create_recall_evaluator("prepare", okay=1)
evaluator_recall_valid = create_recall_evaluator("legitimate", okay=1)
print("Prepare:", evaluator_recall_train(mannequin))
print("Legitimate:", evaluator_recall_valid(mannequin))
# >> Prepare: {'yt-title-thumbnail-train_Recall@1': 0.660377358490566}
# >> Legitimate: {'yt-title-thumbnail-valid_Recall@1': 0.6363636363636364}
We are able to see the mannequin already has first rate 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, decide a loss operate, 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 we’ve so few coaching examples, limiting the variety of parameters we prepare is a good suggestion. On this case, I solely prepare the ultimate projection layer of the mannequin, which maps the textual content and picture embeddings right into a shared vector area. That is about 1M parameters whole.
# import mannequin
from sentence_transformers import SentenceTransformer
mannequin = SentenceTransformer("sentence-transformers/clip-ViT-L-14")# decide particular layers to coach (observe: you'll be able to add extra layers to this record)
trainable_layers_list = ['projection']
# Apply freezing configuration
for title, param in mannequin.named_parameters():
# freeze all params
param.requires_grad = False
# unfreeze layers in trainable_layers_list
if any(layer in title for layer in trainable_layers_list):
param.requires_grad = True
# Rely whole 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 operate
Right here, I take advantage of the A number of Negatives Rating 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 destructive pairs. Right here’s what the loss operate appears like for the one destructive 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 prepare 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.prepare()
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 maintaining these experiments so simple as doable. If you end up spending an excessive amount of time tweaking coaching args to get your mannequin to converge, there’s most likely one thing basically mistaken along with your knowledge (talking from expertise 😅).
As a closing step, we will consider the mannequin’s Recall@1 rating on the testing set. These knowledge 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("Prepare:", evaluator_recall_train(mannequin))
print("Legitimate:", evaluator_recall_valid(mannequin))
print("Take a look at:", evaluator_recall_test(mannequin))
# >> Prepare: {'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 effectively 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 authentic title. Moreover, the recall for the validation dataset will increase by 27%!
Multimodal embedding fashions, like CLIP, unlock numerous 0-shot use circumstances equivalent 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 as we speak’s requirements (~500M parameters) and our coaching dataset was tiny, the ultimate mannequin nonetheless demonstrated robust efficiency on this job. This highlights the ability of fine-tuning.
When you have any questions or solutions for future content material, let me know within the feedback 🙂
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