yagebin/fine-tuned-distilbert-base-uncased-LLM-Judge

Model Card for Model ID

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Model Details

Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

• —Developed by: [Ruiqi Tan]
• —Model type: [Text categorization]
• —License: [Apache 2.0]
• —**Finetuned from model [distilbert-base-uncased]

Uses

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Direct Use

The task is to predict user preferences based on provided input prompts and chatbot responses.

Out-of-Scope Use

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[To estimate wihch text could be more prefereable based on large scale survey]

Bias, Risks, and Limitations

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[Language and usage cultural zone and education level bias]

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

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[The training data can be found on Kaggle competition page: https://www.kaggle.com/competitions/llm-classification-finetuning/data]

Training Procedure

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Preprocessing [optional]

[The dataset is processed to encode modela and modelb as embeddings, while tokenizing textual data using DistilBERT's tokenizer.]

Evaluation

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The dataset was split into 80% training and 20% testing. The categorical embeddings were trained alongside the transformer model for joint optimization.

Metrics

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[At the end of each epoch, validation loss and accuracy were calculated to evaluate the model's performance. These metrics are included in the repository.]

Results

[The model achieved accuracy arund 45%]

Model Architecture and Objective

[The solution combines the power of a pretrained transformer model with categorical embeddings:

Textual Features: The prompt, responsea, and responseb were tokenized using the distilbert-base-uncased tokenizer and processed through a pretrained DistilBERT model.

Categorical Features: The categorical variables modela and modelb were encoded into numeric IDs and processed using separate embedding layers.

Combined Features: The outputs from the transformer model (textual features) and the embeddings (categorical features) were concatenated and passed through a fully connected classifier to predict user preference.]

Hardware

[Hardware Overview:

Model Name: MacBook Pro Model Identifier: MacBookPro16,2 Processor Name: Quad-Core Intel Core i7 Processor Speed: 2,3 GHz Number of Processors: 1 Total Number of Cores: 4 L2 Cache (per Core): 512 KB L3 Cache: 8 MB Hyper-Threading Technology: Enabled Memory: 32 GB System Firmware Version: 2069.80.3.0.0 (iBridge: 22.16.13040.5.1,0) OS Loader Version: 582~3227 Serial Number (system): C02CX487ML85 Hardware UUID: 23703B16-1430-55A6-96D3-B1D3121CDD33 Provisioning UDID: 23703B16-1430-55A6-96D3-B1D3121CDD33 Activation Lock Status: Enabled]

Model Card Contact

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