Create README.md

README.md

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+# Model Card: BART-based Sentiment Classification Model
+LABEL_0 
+LABEL_1 
+LABEL_2
+
+# 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, LABEL_1, or LABEL_2)
+
+# 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}")