README.md

---
license: apache-2.0
datasets:
- AiresPucrs/sentiment-analysis
language:
- en
metrics:
- accuracy
library_name: keras
---
# English Embedding v.16 (Teeny-Tiny Castle)

This model is part of a tutorial tied to the [Teeny-Tiny Castle](https://github.com/Nkluge-correa/TeenyTinyCastle), an open-source repository containing educational tools for AI Ethics and Safety research. 

## How to Use

```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()))

```