Update README.md

README.md

@@ -1,3 +1,113 @@
 ---
 license: apache-2.0
+datasets:
+- AiresPucrs/sentiment-analysis
+language:
+- en
+metrics:
+- accuracy
+library_name: keras
 ---
+# english-embedding-vocabulary-16
+
+## Model Overview
+
+The english-embedding-vocabulary-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. See the [LICENSE](LICENSE) file for more details.