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llm-course-hw3-lora

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 library_name: transformers
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 ---
 library_name: transformers
+datasets:
+- cardiffnlp/tweet_eval
+language:
+- en
+metrics:
+- f1
+base_model:
+- OuteAI/Lite-Oute-1-300M-Instruct
+pipeline_tag: text-classification
 ---
+# Model Card for Lora-adopted Lite-Oute-1-300M-Instruct
+The model was trained with LoRA adapter to classify the sentiment of twitter messages into 'positive', 'negative', and 'neutral'. It was trained on cardiffnlp/tweet_eval dataset.
+LoRA-adopted layers include k_proj and v_proj weight matrices for all attention layers.
 ## Model Details
+The system prompt for the model is as follows:
+>You are a helpful assistant that classifies the sentiment of a message. Classify the sentiment of the given message as exactly one word: 'negative', 'neutral', or 'positive'. Be brief, respond with exactly one word.
+Inputs for the model should be provided in the following format:
+>Message: "[text of the message]"
+>
+The model is trained to output labels in the following format:
+>The sentiment of the message is [label].
+where [label] is either 'positive', 'negative' or 'neutral'.
+Labels can be extracted from the model's outputs with the following function:
+~~~python
+import re
+def postprocess_sentiment(output_text: str) -> str:
+    """
+    Extracts the sentiment classification ("positive" or "negative") from the model's output text.
+    Process:
+        1. Splits the output at the first occurrence of the keyword "assistant" and processes the text after it.
+        2. Uses a regular expression to search for the first occurrence of the words "positive" or "negative" (ignoring case).
+        3. Returns the found sentiment in lowercase. If no match is found, returns an empty string.
+    Parameters:
+        output_text (str): The complete text output from the model, including conversation headers.
+    Returns:
+        str: The sentiment classification or empty string
+    """
+    parts = output_text.split("assistant", 1)
+    text_to_process = parts[0] if len(parts) > 1 else output_text
+    text_to_process = text_to_process.lower()
+    match = re.search(rf"\b({'|'.join(IDX2NAME.values())})\b", text_to_process, re.IGNORECASE)
+    return match.group(1).lower() if match else ""
+~~~
 ## Training Details
+Only k_proj and v_proj layers were adopted. LoRA layers are of rank=8 and use scaling factor alpha=16.
+Model was trained for 1 epoch with learning rate=5e-4 and batch_size=16. Final loss (CrossEntropy) was 0.0673.
 ## Evaluation
+Confusion matrix calculated on the test set is presented below:
+![lora_res.png](lora_res.png)
+It corresponds to macro f1-score of 0.52.
+## Examples of outputs:
+Input (correct label is 'positive'):
+>Message: "I think I may be finally in with the in crowd #mannequinchallenge  #grads2014 @user"
+Output:
+>"The sentiment of the message is positive"
+Input (correct label is 'neutral'):
+>Message: "@user @user That's coming, but I think the victims are going to be Medicaid recipients."
+Output:
+>"The sentiment of the message is neutral"
+Input (correct label is 'negative'):
+>Message: "@user Wow,first Hugo Chavez and now Fidel Castro. Danny Glover, Michael Moore, Oliver Stone, and Sean Penn are running out of heroes."
+Output:
+>"The sentiment of the message is positive"