Model Card for sentence-transformers-for-sentiment-classification-minilm

This is homework for CS 546 Adv topics in NLP, and this is a sentence-transformers/all-MiniLM-L6-v2 model fine-tuned for binary sentiment classification on the IMDB movie reviews dataset.

Model Details

Model Description

This model is a fine-tuned version of the sentence-transformers/all-MiniLM-L6-v2 model, adapted for sequence classification. It has been trained to classify movie reviews from the IMDB dataset as either positive or negative.

• Developed by: Ian
• Model type: BERT-based text classification
• Language(s) (NLP): English
• License: Apache 2.0
• Finetuned from model: sentence-transformers/all-MiniLM-L6-v2

How to Get Started with the Model

Use the code below to get started with the model using the transformers library pipeline.

from transformers import pipeline

classifier = pipeline("sentiment-analysis", model="YourHuggingFaceUsername/sentence-transformers-for-sentiment-classification-minilm")

results = classifier([
    "This movie was absolutely fantastic! The acting was superb and the plot was gripping.",
    "I was really disappointed with this film. It was boring and the story made no sense."
])

for result in results:
    print(result)

# Expected output:
# {'label': 'LABEL_1', 'score': ...}  # Corresponds to positive
# {'label': 'LABEL_0', 'score': ...}  # Corresponds to negative

Training Details

Training Data

The model was fine-tuned on the stanfordnlp/imdb dataset. The training was performed on the train split, which contains 25,000 movie reviews. A 90/10 split was created from this data for training (22,500 examples) and validation (2,500 examples).

Training Procedure

The model was trained for 5 epochs using the AdamW optimizer.

Training Hyperparameters

• Epochs: 5
• Optimizer: AdamW
• Learning rate: 2e-5
• Training batch size: 32

Evaluation

Testing Data

The model was evaluated on the test split of the stanfordnlp/imdb dataset, which consists of 25,000 unseen movie reviews.

Metrics

The primary metric used for evaluation was Accuracy, which measures the percentage of reviews correctly classified. Other metrics computed include F1, Precision, and Recall.

Results

The fine-tuning process resulted in a significant performance improvement over a baseline classifier (Logistic Regression) that used frozen embeddings from the base model.

Environmental Impact

• Hardware Type: NVIDIA A100 GPU
• Hours used: Approximately 0.1 hours (6-7 minutes)
• Cloud Provider: Google Colab
• Carbon Emitted: Carbon emissions can be estimated using the Machine Learning Impact calculator.

Citation

If you use this model, please consider citing the original Sentence-BERT paper:

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "[http://arxiv.org/abs/1908.10084](http://arxiv.org/abs/1908.10084)",
}