| ---
|
| library_name: transformers
|
| tags:
|
| - sentiment-analysis
|
| - bert
|
| - fine-tuned-model
|
| - NLP
|
| license: apache-2.0
|
| language:
|
| - en
|
| base_model:
|
| - google-bert/bert-base-uncased
|
| datasets:
|
| - adilbekovich/Sentiment140Twitter
|
| ---
|
| |
| # Model Card for SentimentBERT |
|
|
| This model is a fine-tuned version of `bert-base-uncased` for sentiment analysis. It has been trained on the **Sentiment140 Kaggle dataset**, enabling it to classify text as **positive** or **negative**. |
|
|
| ## Model Details |
|
|
| ### Model Description |
|
|
| This model is fine-tuned using the `bert-base-uncased` architecture to perform sentiment analysis. It accepts text input and predicts whether the sentiment expressed in the text is positive or negative. |
|
|
| - **Developed by:** Debopam(Pritam) Dey |
| - **Funded by [optional]:** Not specified |
| - **Shared by [optional]:** Debopam(Pritam) Dey |
| - **Model type:** Sequence classification (binary sentiment analysis) |
| - **Language(s) (NLP):** English |
| - **License:** Apache 2.0 |
| - **Finetuned from model [optional]:** bert-base-uncased |
|
|
| ### Model Sources [optional] |
|
|
| - **Repository:** [SentimentBERT](https://huggingface.co/pritam2014/SentimentBERT) |
| - **Demo [optional]:** Coming Soon |
|
|
| ## Uses |
|
|
| Here’s how to use the model for sentiment analysis: |
|
|
| ```python |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| import torch |
| |
| # Load the model and tokenizer from the Hugging Face model hub |
| mymodel = AutoModelForSequenceClassification.from_pretrained("pritam2014/SentimentBERT") |
| mytokenizer = AutoTokenizer.from_pretrained("pritam2014/SentimentBERT") |
| |
| # Preprocess the text input |
| def preprocess_text(text): |
| inputs = mytokenizer.encode_plus( |
| text, |
| max_length=50, |
| padding='max_length', |
| truncation=True, |
| return_attention_mask=True, |
| return_tensors='pt' |
| ) |
| return inputs |
| |
| # Predict sentiment |
| def make_prediction(text): |
| inputs = preprocess_text(text) |
| with torch.no_grad(): |
| outputs = mymodel(inputs['input_ids'], attention_mask=inputs['attention_mask']) |
| logits = outputs.logits |
| predicted_class_id = torch.argmax(logits).item() |
| sentiment_labels = {0: 'Negative', 1: 'Positive'} |
| return sentiment_labels[predicted_class_id] |
| |
| # Example |
| text = "I love this product!" |
| print(make_prediction(text)) # Output: Positive |
| ``` |
| <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
|
|
| ### Direct Use |
| The model can be used for text classification tasks without additional fine-tuning. |
| ```python |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| |
| tokenizer = AutoTokenizer.from_pretrained("pritam2014/SentimentBERT") |
| model = AutoModelForSequenceClassification.from_pretrained("pritam2014/SentimentBERT") |
| |
| from transformers import pipeline |
| |
| # Initialize pipeline |
| sentiment_pipeline = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer) |
| |
| # Example input |
| tweets = [ |
| "I love this product!", |
| "I'm not happy with the service.", |
| "It's okay, could be better." |
| ] |
| |
| # Predict sentiment |
| results = sentiment_pipeline(tweets) |
| for tweet, result in zip(tweets, results): |
| print(f"Tweet: {tweet}\nSentiment: {result['label']}, Score: {result['score']:.4f}\n") |
| ``` |
| <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
|
|
|
|
| ### Downstream Use [optional] |
|
|
| <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
|
|
| Users can fine-tune the model on other sentiment datasets or adapt it for related tasks like emotion detection. |
|
|
| ### Out-of-Scope Use |
|
|
| <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
|
|
| The model is not suitable for multilingual sentiment analysis or highly nuanced text where sentiment depends on complex context. |
|
|
| ## Bias, Risks, and Limitations |
|
|
| <!-- This section is meant to convey both technical and sociotechnical limitations. --> |
|
|
| - The model may inherit biases present in the Sentiment140 dataset. |
| - It is designed for English text and may perform poorly on non-English or mixed-language text. |
|
|
|
|
| ### Recommendations |
|
|
| <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
|
|
| Use the model in scenarios where binary sentiment classification is sufficient. Avoid deploying it in critical systems without further testing for biases and limitations. |
|
|
| ## How to Get Started with the Model |
|
|
| Refer to the "Uses" section above to see the sample usage code. For more details, visit the Hugging Face Hub page. |
|
|
|
|
| ## Training Details |
|
|
| ### Training Data |
|
|
| <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
|
|
| The model was fine-tuned on the Sentiment140 dataset, which contains 1.6 million tweets labelled as positive or negative. |
|
|
| ### Training Procedure |
|
|
| <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
| - Optimizer: AdamW |
| - Batch size: 760 |
| - Learning rate: 1e-5 |
| - Epochs: 2 |
| - Hardware: Kaggle T4 GPU |
|
|
| #### Preprocessing [optional] |
|
|
| [More Information Needed] |
|
|
|
|
| #### Training Hyperparameters |
|
|
| - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
|
|
| #### Speeds, Sizes, Times [optional] |
|
|
| <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
|
|
| [More Information Needed] |
|
|
| ## Evaluation |
|
|
| <!-- This section describes the evaluation protocols and provides the results. --> |
| The model was evaluated on a validation split of the Sentiment140 dataset. |
|
|
| ### Testing Data, Factors & Metrics |
|
|
| #### Testing Data |
|
|
| <!-- This should link to a Dataset Card if possible. --> |
|
|
| [More Information Needed] |
|
|
| #### Factors |
|
|
| <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
|
|
| [More Information Needed] |
|
|
| #### Metrics |
|
|
| <!-- These are the evaluation metrics being used, ideally with a description of why. --> |
|
|
| [More Information Needed] |
|
|
| ### Results |
|
|
| [More Information Needed] |
|
|
| #### Summary |
|
|
|
|
|
|
| ## Model Examination [optional] |
|
|
| <!-- Relevant interpretability work for the model goes here --> |
|
|
| [More Information Needed] |
|
|
| ## Environmental Impact |
|
|
| <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
|
|
| Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |
|
|
| - **Hardware Type:** [More Information Needed] |
| - **Hours used:** [More Information Needed] |
| - **Cloud Provider:** Kaggle T4 GPU |
| - **Compute Region:** [More Information Needed] |
| - **Carbon Emitted:** [More Information Needed] |
|
|
| ## Technical Specifications [optional] |
|
|
| ### Model Architecture and Objective |
|
|
| [More Information Needed] |
|
|
| ### Compute Infrastructure |
|
|
| [More Information Needed] |
|
|
| #### Hardware |
|
|
| [More Information Needed] |
|
|
| #### Software |
|
|
| [More Information Needed] |
|
|
| ## Citation [optional] |
|
|
| <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
|
|
| **BibTeX:** |
|
|
| @misc{pritam2014SentimentBERT, |
| author = {Debopam(Pritam) Dey}, |
| title = {SentimentBERT}, |
| year = {2025}, |
| publisher = {Hugging Face}, |
| howpublished = {\url{https://huggingface.co/pritam2014/SentimentBERT}}, |
| } |
|
|
|
|
| **APA:** |
|
|
| [More Information Needed] |
|
|
| ## Glossary [optional] |
|
|
| <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
|
|
| [More Information Needed] |
|
|
| ## More Information [optional] |
|
|
| The model performs well on short texts like tweets but may require further fine-tuning for longer or domain-specific text. |
|
|
| ## Model Card Authors [optional] |
|
|
| [More Information Needed] |
|
|
| ## Model Card Contact |
|
|
| For questions or feedback, feel free to contact me via the Hugging Face repository or email at (letsdecode2014@gmail.com) |
