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--- |
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license: apache-2.0 |
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datasets: |
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- AiresPucrs/sentiment-analysis |
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language: |
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- en |
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metrics: |
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- accuracy |
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library_name: keras |
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--- |
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# Embedding-model-16 |
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## Model Overview |
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The Embedding-model-16 is a language model for sentiment analysis. |
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### Details |
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- **Size:** 160,289 parameters |
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- **Model type:** word embeddings |
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- **Optimizer**: Adam |
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- **Number of Epochs:** 20 |
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- **Embedding size:** 16 |
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- **Hardware:** Tesla V4 |
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- **Emissions:** Not measured |
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- **Total Energy Consumption:** Not measured |
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### How to Use |
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To run inference on this model, you can use the following code snippet: |
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```python |
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import numpy as np |
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import tensorflow as tf |
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from huggingface_hub import hf_hub_download |
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# Download the model |
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hf_hub_download(repo_id="AiresPucrs/english-embedding-vocabulary-16", |
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filename="english_embedding_vocabulary_16.keras", |
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local_dir="./", |
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repo_type="model" |
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) |
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# Download the embedding vocabulary txt file |
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hf_hub_download(repo_id="AiresPucrs/english-embedding-vocabulary-16", |
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filename="english_embedding_vocabulary.txt", |
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local_dir="./", |
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repo_type="model" |
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) |
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model = tf.keras.models.load_model('english_embedding_vocabulary_16.keras') |
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# Compile the model |
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model.compile(loss='binary_crossentropy', |
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optimizer='adam', |
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metrics=['accuracy']) |
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with open('english_embedding_vocabulary.txt', encoding='utf-8') as fp: |
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english_embedding_vocabulary = [line.strip() for line in fp] |
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fp.close() |
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embeddings = model.get_layer('embedding').get_weights()[0] |
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words_embeddings = {} |
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# iterating through the elements of list |
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for i, word in enumerate(english_embedding_vocabulary): |
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# here we skip the embedding/token 0 (""), because is just the PAD token. |
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if i == 0: |
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continue |
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words_embeddings[word] = embeddings[i] |
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print("Embeddings Dimensions: ", np.array(list(words_embeddings.values())).shape) |
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print("Vocabulary Size: ", len(words_embeddings.keys())) |
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``` |
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## Intended Use |
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This model was created for research purposes only. We do not recommend any application of this model outside this scope. |
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## Performance Metrics |
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The model achieved an accuracy of 84% on validation data. |
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## Training Data |
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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/): |
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- 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._ |
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- 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)._ |
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- 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)._ |
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- The `EcoPreprocessed` [dataset](https://www.kaggle.com/datasets/pradeeshprabhakar/preprocessed-dataset-sentiment-analysis): _scrapped amazon product reviews_. |
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## Limitations |
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We do not recommend using this model in real-world applications. It was solely developed for academic and educational purposes. |
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## Cite as |
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```latex |
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@misc{teenytinycastle, |
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doi = {10.5281/zenodo.7112065}, |
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url = {https://github.com/Nkluge-correa/teeny-tiny_castle}, |
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author = {Nicholas Kluge Corr{\^e}a}, |
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title = {Teeny-Tiny Castle}, |
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year = {2024}, |
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publisher = {GitHub}, |
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journal = {GitHub repository}, |
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} |
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``` |
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## License |
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This model is licensed under the Apache License, Version 2.0. |
