transformers safetensors gpt_neox text-classification generated_from_trainer dataset:allenai/ultrafeedback_binarized_cleaned base_model:kykim0/pythia-1b-tulu-v2-mix base_model:finetune:kykim0/pythia-1b-tulu-v2-mix license:apache-2.0 model-index autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model: kykim0/pythia-1b-tulu-v2-mix
datasets:
- allenai/ultrafeedback_binarized_cleaned
library_name: transformers
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: b32-lr1.41e-05-s0-e2-btbinf-seed42
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: allenai/ultrafeedback_binarized_cleaned
      type: allenai/ultrafeedback_binarized_cleaned
    metrics:
    - type: accuracy
      value: 0.7427109974424553
      name: Accuracy
---

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

This model is a fine-tuned version of [kykim0/pythia-1b-tulu-v2-mix](https://huggingface.co/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
