Embedding Classifier
Learn how to train an embedding classifier using Clarifai Python SDK
An Embedding Classifier is a machine learning model that combines the concept of word embeddings with classification tasks. Word embeddings are dense vector representations of words in a high-dimensional space, typically generated by algorithms like Word2Vec, GloVe, or FastText. You can learn more about Embedding Classifier here.
App Creation
The first part of model training includes the creation of an app under which the training process takes place. Here we are creating an app with the app id as “demo_train” and the base workflow is set as “Universal”. You can change the base workflows to Empty, Universal, Language Understanding, and General according to your use case.
- Python
from clarifai.client.user import User
#replace your "user_id"
client = User(user_id="user_id")
app = client.create_app(app_id="demo_train", base_workflow="Universal")
Dataset Upload
The next step involves dataset upload. You can upload the dataset to your app so that the model accepts the data directly from the platform. The data used for training in this tutorial is available in the examples repository you have cloned.
- Python
#importing load_module_dataloader for calling the dataloader object in dataset.py in the local data folder
from clarifai.datasets.upload.utils import load_module_dataloader
# Construct the path to the dataset folder
CSV_PATH = os.path.join(os.getcwd().split('/models/model_train')[0],'datasets/upload/data/imdb.csv')
# Create a Clarifai dataset with the specified dataset_id
dataset = app.create_dataset(dataset_id="text_dataset")
# Upload the dataset using the provided dataloader and get the upload status
dataset.upload_from_csv(csv_path=CSV_PATH,input_type='text',csv_type='raw', labels=True)
If you have followed the steps correctly you should receive an output that looks like this,
Output
Choose The Model Type
First let's list the all available trainable model types in the platform,
- Python
print(app.list_trainable_model_types())
Output
['visual-classifier',
'visual-detector',
'visual-segmenter',
'visual-embedder',
'clusterer',
'text-classifier',
'embedding-classifier',
'text-to-text']
Click here to know more about Clarifai Model Types.
Model Creation
From the above list of model types we are going to choose embedding-classifier as it is similar to our use case. Now let's create a model with the above model type.
- Python
MODEL_ID = "model_text_embedder"
MODEL_TYPE_ID = "embedding-classifier"
# Create a model by passing the model name and model type as parameter
model = app.create_model(model_id=MODEL_ID, model_type_id=MODEL_TYPE_ID)
Output
Template Selection
Inside the Clarifai platform there is a template feature. Templates give you the control to choose the specific architecture used by your neural network, as well as define a set of hyperparameters you can use to fine-tune the way your model learns. But when it comes to Embedding Classifier there is only one default template available.
Setup Model Parameters
You can update the model parameters to your needs before initiating training.
- Python
# get the model params for default template
model_params = model.get_params()
concepts = [concept.id for concept in app.list_concepts()]
# update the concept field in model_params
model.update_params(dataset_id = 'text_dataset',concepts = ["id-pos","id-neg"])
Output
{'dataset_id': 'text_dataset',
'dataset_version_id': '',
'concepts': ['id-pos', 'id-neg'],
'train_params': {'base_embed_model': None, 'enrich_dataset': 'Automatic'},
'inference_params': {'min_value': 0.0}}
Initiate Model Training
We can initiate the model training by calling the model.train() method. The Clarifai Python SDK also offers features like showing training status and saving training logs in a local file.
If the status code is 'MODEL-TRAINED', then the user can know the Model is Trained and ready to use.
- Python
import time
#Starting the training
model_version_id = model.train()
#Checking the status of training
while True:
status = model.training_status(version_id=model_version_id,training_logs=False)
if status.code == 21106: #MODEL_TRAINING_FAILED
print(status)
break
elif status.code == 21100: #MODEL_TRAINED
print(status)
break
else:
print("Current Status:",status)
print("Waiting---")
time.sleep(120)
Output
Model Prediction
Since the model is trained and ready let’s run some predictions to view the model performance,
- Python
# Get the predictions
TEXT = b"This is a great place to work"
model_prediction = model.predict_by_bytes(TEXT, input_type="text")
# Get the output
print('Input: ',TEXT)
for concept in model_prediction.outputs[0].data.concepts:
print(concept.id,':',round(concept.value,2))
Output
Input: b'This is a great place to work'
id-neg : 0.72
id-pos : 0.28
Model Evaluation
Now let's evaluate the model using train and test datasets. First let's see the evaluation metrics for the training dataset,
- Python
# Evaluate the model using the specified dataset ID 'text_dataset' and evaluation ID 'one'.
model.evaluate(dataset_id='text_dataset',eval_id='one',eval_info={"use_kfold":False})
# Retrieve the evaluation result for the evaluation ID 'one'.
result = model.get_eval_by_id(eval_id="one")
# Print the summary of the evaluation result.
print(result.summary)
Output
macro_avg_roc_auc: 1.0
macro_avg_f1_score: 1.0
macro_avg_precision: 1.0
macro_avg_recall: 1.0
Before evaluating with a test dataset, we have to first upload the dataset using the data loader and then perform model evaluation,
- Python
#importing load_module_dataloader for calling the dataloader object in dataset.py in the local data folder
from clarifai.datasets.upload.utils import load_module_dataloader
# Construct the path to the dataset folder
CSV_PATH = os.path.join(os.getcwd().split('/models/model_train')[0],'datasets/upload/data/test_imdb.csv')
# Create a Clarifai dataset with the specified dataset_id
test_dataset = app.create_dataset(dataset_id="test_text_dataset")
# Upload the dataset using the provided dataloader and get the upload status
test_dataset.upload_from_csv(csv_path=CSV_PATH,input_type='text',csv_type='raw', labels=True)
# Evaluate the model using the specified test text dataset identified as 'test_text_dataset'
# and the evaluation identifier 'two'.
model.evaluate(dataset_id='test_text_dataset', eval_id='two')
# Retrieve the evaluation result with the identifier 'two'.
result = model.get_eval_by_id("two")
# Print the summary of the evaluation result.
print(result.summary)
Output
macro_avg_roc_auc: 1.0
macro_avg_f1_score: 1.0
macro_avg_precision: 1.0
macro_avg_recall: 1.0
Finally let's compare the results from multiple datasets using EvalResultCompare feature from Clarifai Python SDK to get a better understanding of the model's performance.
- Python
from clarifai.utils.evaluation import EvalResultCompare
# Creating an instance of EvalResultCompare class with specified models and datasets
eval_result = EvalResultCompare(models=[model], datasets=[dataset, test_dataset])
# Printing a detailed summary of the evaluation result
print(eval_result.detailed_summary())
Output
( Concept Accuracy (ROC AUC) Total Labeled Total Predicted True Positives \
0 id-pos 1.0 80 80 80
0 id-neg 1.0 120 120 120
0 id-pos 1.0 31 31 31
0 id-neg 1.0 40 40 40
False Negatives False Positives Recall Precision F1 Dataset
0 0 0 1.0 1.0 1.0 text_dataset2
0 0 0 1.0 1.0 1.0 text_dataset2
0 0 0 1.0 1.0 1.0 test_text_dataset
0 0 0 1.0 1.0 1.0 test_text_dataset , Total Concept Accuracy (ROC AUC) Total Labeled \
0 Dataset:text_dataset2 1.0 200
0 Dataset:test_text_dataset 1.0 71
Total Predicted True Positives False Negatives False Positives Recall \
0 200 200 0 0 1.0
0 71 71 0 0 1.0
Precision F1
0 1.0 1.0
0 1.0 1.0 )