creyesp

1. install ProjectEnv plugin
2. install direnv
3. create a .env file and configure the env variables
4. create a .envrc file ans write on it dotenv
5. configure in pycharm > setting > build ... > ProjectEnv > add files (.env)

creyesp

The below table shows some examples of heuristic benchmarks to compare the performance of a machine learning model when no previous solution exists. The original version of the table can be found in the Machine Learning Design Patterns Book (pattern 28)

| Scenario | Heuristic benchmark | Example task | Implementation for example task

creyesp

Blogs

• https://medium.datadriveninvestor.com/how-to-build-a-recommendation-system-for-purchase-data-step-by-step-d6d7a78800b6
• https://www.kaggle.com/c/santander-product-recommendation
• https://www.kaggle.com/retailrocket/ecommerce-dataset/home
• https://www.kaggle.com/dschettler8845/recsys-2020-ecommerce-dataset/tasks?taskId=3124
• https://www.kaggle.com/sohamohajeri/recommendation-system-for-electronic-dataset
• https://towardsdatascience.com/extreme-deep-factorization-machine-xdeepfm-1ba180a6de78
• https://medium.com/building-creative-market/word2vec-inspired-recommendations-in-production-f2c6a6b5b0bf
• https://medium.com/shoprunner/fetching-better-beer-recommendations-with-collie-part-1-18c73ab30fbd

creyesp

from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_name = "tr3cks/3LabelsSentimentAnalysisSpanish"
tokenizer_sent_esp = AutoTokenizer.from_pretrained(model_name)
model_sent_esp = AutoModelForSequenceClassification.from_pretrained(model_name)

# The output is ['ja', '##ja', '##ja', 'que', 'risa', 'me', 'da']
tokenizer_sent_esp.tokenize('jajaja que risa me da')