Instructions to use lenatr99/loha_fine_tuned_croslo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use lenatr99/loha_fine_tuned_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_croslo") - Notebooks
- Google Colab
- Kaggle
loha_fine_tuned_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: 0.6890
- Accuracy: 0.52
- F1: 0.5212
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 |
|---|---|---|---|---|---|
| 0.7072 | 1.0 | 50 | 0.6896 | 0.52 | 0.5212 |
| 0.6973 | 2.0 | 100 | 0.6894 | 0.53 | 0.5312 |
| 0.6988 | 3.0 | 150 | 0.6892 | 0.54 | 0.5411 |
| 0.7016 | 4.0 | 200 | 0.6891 | 0.53 | 0.5312 |
| 0.7034 | 5.0 | 250 | 0.6890 | 0.52 | 0.5212 |
| 0.6978 | 6.0 | 300 | 0.6890 | 0.51 | 0.5112 |
| 0.6965 | 7.0 | 350 | 0.6890 | 0.51 | 0.5112 |
| 0.6907 | 8.0 | 400 | 0.6890 | 0.52 | 0.5212 |
Framework versions
- PEFT 0.10.1.dev0
- Transformers 4.40.1
- Pytorch 2.3.0
- Datasets 2.19.0
- Tokenizers 0.19.1
- Downloads last month
- 3
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
Model tree for lenatr99/loha_fine_tuned_croslo
Base model
EMBEDDIA/crosloengual-bert