Instructions to use lenatr99/prompt_fine_tuned_CB_croslo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use lenatr99/prompt_fine_tuned_CB_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/prompt_fine_tuned_CB_croslo") - Notebooks
- Google Colab
- Kaggle
prompt_fine_tuned_CB_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: 1.2046
- Accuracy: 0.3182
- F1: 0.1536
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: 1
- eval_batch_size: 1
- 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 |
|---|---|---|---|---|---|
| 1.0278 | 0.4545 | 50 | 1.1158 | 0.3182 | 0.2306 |
| 0.9865 | 0.9091 | 100 | 1.1195 | 0.3636 | 0.2430 |
| 0.8601 | 1.3636 | 150 | 1.1357 | 0.3182 | 0.1536 |
| 0.8769 | 1.8182 | 200 | 1.1595 | 0.3182 | 0.1536 |
| 0.9026 | 2.2727 | 250 | 1.1733 | 0.3182 | 0.1536 |
| 0.8002 | 2.7273 | 300 | 1.1885 | 0.3182 | 0.1536 |
| 0.8093 | 3.1818 | 350 | 1.1996 | 0.3182 | 0.1536 |
| 0.7259 | 3.6364 | 400 | 1.2046 | 0.3182 | 0.1536 |
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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Model tree for lenatr99/prompt_fine_tuned_CB_croslo
Base model
EMBEDDIA/crosloengual-bert