| # 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}") | |