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---
license: apache-2.0
base_model: studio-ousia/mluke-large-lite
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: out
results: []
---
<!-- 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. -->
## Fine-tuning
- this model was trained to classify whether input text comes from "chosen sentence" or "rejected sentence"
- the probability (logits after passing softmax function) in last layer of this model can be used to quantify the preference from user input
- fine-tuned [studio-ousia/mluke-large-lite](https://huggingface.co/studio-ousia/mluke-large-lite) via full parameter tuning using [open-preference-v0.3](https://huggingface.co/datasets/ryota39/open_preference-v0.3)
- trained on bf16 format
- Label 0 stands for rejected sentence
- Label 1 stands for chosen sentence
- **Note that this model can handle only 512 tokens in maximum**
- **The limitation arises from Luke-based pre-trained model**
## Metric
- train and validation split
|train loss|eval loss|accuracy|recall|precision|f1-score|
|:---|:---|:---|:---|:---|:---|
|0.114|0.1615|0.9399|0.9459|0.9346|0.9402|
- test split
|accuracy|recall|precision|f1-score|
|:---|:---|:---|:---|
|0.9416|0.9319|0.9504|0.9411|
- confusion matrix when test split
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.4109 | 1.0 | 1479 | 0.2462 | 0.9003 | 0.8710 | 0.9399 | 0.9041 |
| 0.1579 | 2.0 | 2958 | 0.1573 | 0.9399 | 0.9495 | 0.9293 | 0.9393 |
| 0.114 | 3.0 | 4437 | 0.1615 | 0.9399 | 0.9346 | 0.9460 | 0.9403 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.1.0+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1
|