dnzblgn/BART_Sentiment_Classification

Model Card: BART-based Sentiment Classification Model

LABEL_0 = NEGATIVE

LABEL_1 = NEUTRAL

LABEL_2 = POSITIVE

Model Details

Model Name: BART-based Sentiment Classification Model Description: This model is trained using BART (Bidirectional and Auto-Regressive Transformers) for sentiment classification on the chat_dataset. It takes a message as input and predicts the sentiment category, which can be one of three labels: LABEL_0, LABEL_1, or LABEL_2. Framework: PyTorch Model Architecture: BARTForSequenceClassification (based on the BART model architecture) Pretrained Model: Facebook's BART-base

Intended Use

Primary Task: Sentiment Classification Input: Textual message (string) Output: Sentiment category label (LABEL_0(NEGATIVE), LABEL_1(NEUTRAL), or LABEL_2(POSITIVE))

Training Data

Dataset: chat_dataset.csv Data Preprocessing: The dataset is loaded from the CSV file. The messages are tokenized using the BART tokenizer, and the sentiment labels are encoded using a LabelEncoder. The dataset is split into training and testing sets. Model Training: The model is trained using the training set with a batch size of 4 and AdamW optimizer. The training loop runs for 5 epochs, optimizing the cross-entropy loss between predicted and true labels.

Evaluation

Evaluation Dataset: The model's performance can be evaluated using the provided testing set or any other suitable dataset with sentiment labels. Expected Performance: The model is expected to achieve reasonable accuracy in sentiment classification based on the quality and representativeness of the training data.

Usage Example

Load the model and tokenizer model = BartForSequenceClassification.from_pretrained("path/to/model/directory") tokenizer = BartTokenizer.from_pretrained("path/to/tokenizer/directory")

Perform sentiment classification on a sample sentence input_text = "This is a great product!" input_tokens = tokenizer.encode_plus(input_text, padding="max_length", truncation=True, max_length=max_length, return_tensors="pt") input_ids = input_tokens["input_ids"].to(device) attention_mask = input_tokens["attention_mask"].to(device)

with torch.no_grad(): outputs = model(input_ids=input_ids, attention_mask=attention_mask) logits = outputs.logits predicted_label = torch.argmax(logits, dim=1).item() sentiment_category = le.inverse_transform([predicted_label])[0]

print(f"Input: {input_text}") print(f"Predicted Sentiment: {sentiment_category}")