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---
library_name: transformers
license: apache-2.0
base_model: kykim0/pythia-1b-tulu-v2-mix-nos
tags:
- trl
- reward-trainer
- generated_from_trainer
datasets:
- allenai/ultrafeedback_binarized_cleaned
metrics:
- accuracy
model-index:
- name: pythia-1b-tulu-v2-mix-nos-rm
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: 0.7457800511508952
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pythia-1b-tulu-v2-mix-nos-rm
This model is a fine-tuned version of [kykim0/pythia-1b-tulu-v2-mix-nos](https://huggingface.co/kykim0/pythia-1b-tulu-v2-mix-nos) on the allenai/ultrafeedback_binarized_cleaned dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5227
- 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: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.5749 | 0.0527 | 100 | 0.5849 | 0.6900 |
| 0.5873 | 0.1055 | 200 | 0.5581 | 0.7100 |
| 0.5599 | 0.1582 | 300 | 0.5470 | 0.7212 |
| 0.5456 | 0.2109 | 400 | 0.5379 | 0.7258 |
| 0.521 | 0.2637 | 500 | 0.5358 | 0.7294 |
| 0.5361 | 0.3164 | 600 | 0.5363 | 0.7376 |
| 0.5662 | 0.3691 | 700 | 0.5270 | 0.7412 |
| 0.5301 | 0.4219 | 800 | 0.5268 | 0.7427 |
| 0.5661 | 0.4746 | 900 | 0.5301 | 0.7381 |
| 0.5608 | 0.5274 | 1000 | 0.5242 | 0.7437 |
| 0.5223 | 0.5801 | 1100 | 0.5242 | 0.7422 |
| 0.5322 | 0.6328 | 1200 | 0.5249 | 0.7448 |
| 0.4891 | 0.6856 | 1300 | 0.5241 | 0.7427 |
| 0.5111 | 0.7383 | 1400 | 0.5234 | 0.7437 |
| 0.5145 | 0.7910 | 1500 | 0.5225 | 0.7422 |
| 0.4746 | 0.8438 | 1600 | 0.5226 | 0.7458 |
| 0.5551 | 0.8965 | 1700 | 0.5223 | 0.7448 |
| 0.563 | 0.9492 | 1800 | 0.5222 | 0.7453 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.19.1
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