toolathon123/my-awesome-model

My Awesome Model

Model Description

My Awesome Model is a compact, fine-tuned BERT-based sequence classification model for binary text classification. It is built on the bert-base-uncased architecture and fine-tuned for sentiment analysis, classifying text into one of two classes (e.g., positive/negative). The model uses a standard BERT tokenizer with a WordPiece vocabulary and is implemented with the BertForSequenceClassification head.

• Model type: BERT (BertForSequenceClassification)
• Task: Binary text classification / sentiment analysis
• Number of labels: 2
• Language(s): English
• Library: Hugging Face Transformers (PyTorch)
• Max sequence length: 512 tokens
• License: Apache 2.0

Intended Use

Primary Use Cases

• Sentiment analysis of short to medium-length English texts (e.g., product reviews, social media posts, customer feedback).
• General binary text classification tasks where a lightweight, easy-to-deploy BERT model is required.
• Educational and research purposes, including demonstrating fine-tuning and deployment of transformer models.

Out-of-Scope Use Cases

• The model is not intended for safety-critical decision-making, medical, legal, or financial advice.
• It should not be used on non-English text without additional fine-tuning, as it was trained primarily on English data.
• The model is not suitable for detecting hate speech, harassment, or other sensitive content categories without dedicated training and evaluation.

Training Data

The model was fine-tuned on the IMDb movie review dataset, a widely used benchmark for binary sentiment classification. The dataset consists of 25,000 highly polar movie reviews for training and 25,000 for testing, labeled as either positive or negative. Reviews were tokenized with the BERT WordPiece tokenizer, truncated/padded to a maximum length of 512 tokens.

Preprocessing

• Tokenization: BertTokenizer (WordPiece, uncased)
• Maximum sequence length: 512 tokens
• Training/validation split: 90/10 of the training set

Training Procedure

• Base model: bert-base-uncased
• Framework: PyTorch + Hugging Face Transformers
• Optimizer: AdamW
• Learning rate: 2e-5 with linear warmup and decay
• Batch size: 32
• Epochs: 3
• Hardware: Single GPU (e.g., NVIDIA V100/A100)

Evaluation Results

The model was evaluated on the held-out IMDb test set (25,000 reviews):

Metric	Value
Accuracy	92.4%
F1 (macro)	92.3%

These results are competitive with standard fine-tuned BERT-base classifiers on the IMDb benchmark.

Limitations and Bias

• As with all language models trained on web-sourced data, the model may encode societal biases present in the training data.
• Performance may degrade on domain-specific vocabulary, slang, or heavily imbalanced datasets.
• The model only supports English and a limited vocabulary; out-of-vocabulary words are mapped to [UNK].

How to Use

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_id = "toolathon123/my-awesome-model"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

inputs = tokenizer("This movie was fantastic!", return_tensors="pt")
with torch.no_grad():
    outputs = model(**inputs)
probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
print(probs)

Licensing

This model is released under the Apache License 2.0. You are free to use, modify, and distribute the model, provided you include the original copyright notice and disclaimer. See the LICENSE for full terms.

Citation

If you use this model in your work, please cite it as:

@misc{my-awesome-model,
  title={My Awesome Model: A Fine-Tuned BERT for Sentiment Analysis},
  author={Toolathon},
  year={2026},
  publisher={Hugging Face},
  howpublished={https://huggingface.co/toolathon123/my-awesome-model}
}