Instructions to use thenlpresearcher/gemma_sequence_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use thenlpresearcher/gemma_sequence_classification with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("google/gemma-2-9b") model = PeftModel.from_pretrained(base_model, "thenlpresearcher/gemma_sequence_classification") - Notebooks
- Google Colab
- Kaggle
| library_name: peft | |
| license: gemma | |
| base_model: google/gemma-2-9b | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: gemma_sequence_classification | |
| 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. --> | |
| # gemma_sequence_classification | |
| This model is a fine-tuned version of [google/gemma-2-9b](https://huggingface.co/google/gemma-2-9b) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.2145 | |
| - Pearson: 0.9718 | |
| ## 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: 0.0001 | |
| - train_batch_size: 32 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 5 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Pearson | | |
| |:-------------:|:-----:|:----:|:---------------:|:-------:| | |
| | No log | 1.0 | 206 | 0.2220 | 0.9313 | | |
| | No log | 2.0 | 412 | 0.2142 | 0.9564 | | |
| | 0.3426 | 3.0 | 618 | 0.1653 | 0.9716 | | |
| | 0.3426 | 4.0 | 824 | 0.2545 | 0.9750 | | |
| | 0.0318 | 5.0 | 1030 | 0.2145 | 0.9718 | | |
| ### Framework versions | |
| - PEFT 0.14.0 | |
| - Transformers 4.45.2 | |
| - Pytorch 2.4.0a0+f70bd71a48.nv24.06 | |
| - Datasets 3.2.0 | |
| - Tokenizers 0.20.3 |