Model Card for BERT

Model Description

This is a BERT model fine-tuned for sentiment analysis. BERT (Bidirectional Encoder Representations from Transformers) is a transformer-based model designed to understand the context of words in search queries.

Intended Use

Primary use case: Sentiment analysis on social media posts.
Limitations: The model may exhibit biases present in the training data and may not perform well on out-of-domain data.

Training Data

This model was trained on the [Stanford Sentiment Treebank]. The dataset consists of 11,855 labeled sentences for sentiment classification.

Evaluation Results

The model achieves the following results on the Stanford Sentiment Treebank:

Accuracy: 92%
F1 Score: 0.91

How to Use

Here’s how to load and use the model in Python:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model_name = "FoundationsofInformationRetrieval/my_model_repo"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("I love using Hugging Face!", return_tensors="pt")
outputs = model(**inputs)