---
base_model: Wonder-Griffin/Judge-GPT2
datasets:
- stanfordnlp/imdb
language:
- en
library_name: transformers
license: unlicense
pipeline_tag: text-generation
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.
tags:
- text-generation-inference
- question-answering
---
# 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