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pythia-1b-tulu-v2-mix-sys-uf-rm

Text Classification
Transformers
Safetensors
gpt_neox
Generated from Trainer
Eval Results
text-generation-inference
Inference Endpoints
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metadata
library_name: transformers
license: apache-2.0
base_model: kykim0/pythia-1b-tulu-v2-mix
tags:
  - generated_from_trainer
datasets:
  - allenai/ultrafeedback_binarized_cleaned
metrics:
  - accuracy
model-index:
  - name: b32-lr1.41e-05-s0-e2-btbinf-seed42
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: allenai/ultrafeedback_binarized_cleaned
          type: allenai/ultrafeedback_binarized_cleaned
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7427109974424553

b32-lr1.41e-05-s0-e2-btbinf-seed42

This model is a fine-tuned version of kykim0/pythia-1b-tulu-v2-mix on the allenai/ultrafeedback_binarized_cleaned dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5040
  • Accuracy: 0.7458

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: 1.41e-05
  • train_batch_size: 1
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 32
  • total_eval_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 1

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.6627 0.0527 100 0.6306 0.6675
0.5604 0.1055 200 0.5954 0.6890
0.5743 0.1582 300 0.5773 0.6880
0.573 0.2110 400 0.5408 0.7182
0.5644 0.2637 500 0.5285 0.7361
0.5482 0.3165 600 0.5251 0.7366
0.5673 0.3692 700 0.5267 0.7279
0.5701 0.4219 800 0.5123 0.7453
0.5199 0.4747 900 0.5148 0.7376
0.5525 0.5274 1000 0.5133 0.7494
0.5197 0.5802 1100 0.5085 0.7488
0.4977 0.6329 1200 0.5146 0.7412
0.492 0.6857 1300 0.5116 0.7417
0.5046 0.7384 1400 0.5069 0.7453
0.5476 0.7911 1500 0.5044 0.7478
0.5247 0.8439 1600 0.5038 0.7468
0.5591 0.8966 1700 0.5079 0.7453
0.5228 0.9494 1800 0.5040 0.7458
0.5336 1.0021 1900 0.5045 0.7488

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.1.2+cu121
  • Datasets 2.14.6
  • Tokenizers 0.19.1