metadata
license: apache-2.0
base_model: studio-ousia/luke-japanese-base-lite
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: out
results: []
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 via full parameter tuning using 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 |
|---|---|---|---|---|---|
| 0.1427 | 0.2009 | 9282 | 0.9383 | 0.9198 | 0.9290 |
- test split
| accuracy | recall | precision | f1-score |
|---|---|---|---|
| 0.9310 | 0.9199 | 0.9408 | 0.9302 |
- 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 |
|---|---|---|---|---|---|---|---|
| 0.316 | 1.0 | 1479 | 0.2245 | 0.9127 | 0.9027 | 0.9251 | 0.9138 |
| 0.1696 | 2.0 | 2958 | 0.1869 | 0.9308 | 0.9234 | 0.9395 | 0.9314 |
| 0.1427 | 3.0 | 4437 | 0.2009 | 0.9283 | 0.9198 | 0.9384 | 0.9290 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.1.0+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1