---
base_model: Wonder-Griffin/Judge-GPT2
datasets:
- stanfordnlp/imdb
language:
- en
library_name: transformers
license: unlicense
pipeline_tag: text-generation
model-index:
- name: JudgeLLM
  results: []
tags:
- text-generation-inference
- question-answering
---
model-index:
  - name: Wonder-Griffin/JudgeLLM
    results:
      - task:
          type: text-classification
          name: Text Classification
          modality: nlp
        subtasks:
          - type: acceptability-classification
            name: Acceptability Classification
          - type: sentiment-analysis
            name: Sentiment Analysis
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.95
      - task:
          type: summarization
          name: Summarization
          modality: nlp
        metrics:
          - name: Rouge-L
            type: rouge
            value: 0.8
inference:
  parameters:
    aggregation_strategy: "simple"
    top_k: 10
    top_p: 0.9
    temperature: 0.7
    max_new_tokens: 50
    do_sample: true
    guidance_scale: 7.5
    num_inference_steps: 50
example_inputs:
  - text: "Sample input text for text classification"
    table:
      headers: ["Column 1", "Column 2"]
      rows:
        - ["Data 1", "Data 2"]
    src: "path/to/asset"
    prompt: "Generate an image with the following prompt..."
    candidate_labels: ["positive", "negative"]
    multi_class: true
    messages:
      - role: user
        content: "What is the weather like today?"
      - role: assistant
        content: "The weather is sunny with a chance of rain."
---
---
# JudgeLLM

This model is a fine-tuned version of [Wonder-Griffin/Judge-GPT2](https://huggingface.co/Wonder-Griffin/Judge-GPT2) on 
## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results



### Framework versions

- Transformers 4.43.3
- Pytorch 2.4.0+cu124
- Datasets 2.20.0
- Tokenizers 0.19.1