---
license: apache-2.0
datasets:
- AiresPucrs/sentiment-analysis
language:
- en
metrics:
- accuracy
library_name: keras
---
# Embedding-model-16

## Model Overview

The Embedding-model-16 is a language model for sentiment analysis.

### Details

- **Size:** 160,289  parameters
- **Model type:** word embeddings
- **Optimizer**: Adam
- **Number of Epochs:** 20
- **Embedding size:** 16
- **Hardware:** Tesla V4
- **Emissions:** Not measured
- **Total Energy Consumption:** Not measured

### How to Use

To run inference on this model, you can use the following code snippet:

```python
import numpy as np
import tensorflow as tf
from huggingface_hub import hf_hub_download

# Download the model
hf_hub_download(repo_id="AiresPucrs/english-embedding-vocabulary-16",
                filename="english_embedding_vocabulary_16.keras",
                local_dir="./",
                repo_type="model"
                )

# Download the embedding vocabulary txt file
hf_hub_download(repo_id="AiresPucrs/english-embedding-vocabulary-16",
                filename="english_embedding_vocabulary.txt",
                local_dir="./",
                repo_type="model"
                )

model = tf.keras.models.load_model('english_embedding_vocabulary_16.keras')

# Compile the model
model.compile(loss='binary_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

with open('english_embedding_vocabulary.txt', encoding='utf-8') as fp:
    english_embedding_vocabulary = [line.strip() for line in fp]
    fp.close()

embeddings = model.get_layer('embedding').get_weights()[0]

words_embeddings = {}

# iterating through the elements of list
for i, word in enumerate(english_embedding_vocabulary):
    # here we skip the embedding/token 0 (""), because is just the PAD token.
    if i == 0:
        continue
    words_embeddings[word] = embeddings[i]

print("Embeddings Dimensions: ", np.array(list(words_embeddings.values())).shape)
print("Vocabulary Size: ", len(words_embeddings.keys()))
```
## Intended Use

This model was created for research purposes only. We do not recommend any application of this model outside this scope.

## Performance Metrics

The model achieved an accuracy of 84% on validation data.

## Training Data

The model was trained using a dataset that was put together by combining several datasets for sentiment classification available on [Kaggle](https://www.kaggle.com/):

- The `IMDB 50K` [dataset](https://www.kaggle.com/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews?select=IMDB+Dataset.csv): _0K movie reviews for natural language processing or Text analytics._
- The `Twitter US Airline Sentiment` [dataset](https://www.kaggle.com/datasets/crowdflower/twitter-airline-sentiment): _originated from the  [Crowdflower's Data for Everyone library](http://www.crowdflower.com/data-for-everyone)._
- Our `google_play_apps_review` _dataset: built using the `google_play_scraper` in [this notebook](https://github.com/Nkluge-correa/teeny-tiny_castle/blob/master/ML%20Explainability/NLP%20Interpreter%20(en)/scrape(en).ipynb)._
- The `EcoPreprocessed` [dataset](https://www.kaggle.com/datasets/pradeeshprabhakar/preprocessed-dataset-sentiment-analysis): _scrapped amazon product reviews_.

## Limitations

We do not recommend using this model in real-world applications. It was solely developed for academic and educational purposes.

## Cite as

```latex
@misc{teenytinycastle,
    doi = {10.5281/zenodo.7112065},
    url = {https://github.com/Nkluge-correa/teeny-tiny_castle},
    author = {Nicholas Kluge Corr{\^e}a},
    title = {Teeny-Tiny Castle},
    year = {2024},
    publisher = {GitHub},
    journal = {GitHub repository},
}
```

## License

This model is licensed under the Apache License, Version 2.0.