Instructions to use lenatr99/loha_fine_tuned_boolq_croslo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use lenatr99/loha_fine_tuned_boolq_croslo with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("EMBEDDIA/crosloengual-bert") model = PeftModel.from_pretrained(base_model, "lenatr99/loha_fine_tuned_boolq_croslo") - Notebooks
- Google Colab
- Kaggle
loha_fine_tuned_boolq_croslo
This model is a fine-tuned version of EMBEDDIA/crosloengual-bert on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5904
- Accuracy: 0.8333
- F1: 0.8243
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 400
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.7131 | 4.1667 | 50 | 0.6457 | 0.7222 | 0.7325 |
| 0.676 | 8.3333 | 100 | 0.6157 | 0.7222 | 0.7325 |
| 0.6777 | 12.5 | 150 | 0.6047 | 0.7778 | 0.7778 |
| 0.687 | 16.6667 | 200 | 0.5982 | 0.7778 | 0.7778 |
| 0.669 | 20.8333 | 250 | 0.5940 | 0.8333 | 0.8243 |
| 0.6743 | 25.0 | 300 | 0.5911 | 0.8333 | 0.8243 |
| 0.6841 | 29.1667 | 350 | 0.5910 | 0.8333 | 0.8243 |
| 0.6639 | 33.3333 | 400 | 0.5904 | 0.8333 | 0.8243 |
Framework versions
- PEFT 0.10.1.dev0
- Transformers 4.40.1
- Pytorch 2.3.0
- Datasets 2.19.0
- Tokenizers 0.19.1
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Base model
EMBEDDIA/crosloengual-bert