Instructions to use mamiksik/Testing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mamiksik/Testing with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mamiksik/Testing")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mamiksik/Testing") model = AutoModelForMaskedLM.from_pretrained("mamiksik/Testing", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: Testing | |
| 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. --> | |
| # Testing | |
| This model is a fine-tuned version of [microsoft/codebert-base-mlm](https://huggingface.co/microsoft/codebert-base-mlm) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.4526 | |
| - Accuracy: 0.9038 | |
| ## 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: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 10 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:-----:|:---------------:|:--------:| | |
| | 0.7258 | 1.0 | 1373 | 0.5835 | 0.8779 | | |
| | 0.5861 | 2.0 | 2746 | 0.5315 | 0.8882 | | |
| | 0.5478 | 3.0 | 4119 | 0.5053 | 0.8941 | | |
| | 0.5338 | 4.0 | 5492 | 0.4929 | 0.8974 | | |
| | 0.5045 | 5.0 | 6865 | 0.4836 | 0.8995 | | |
| | 0.4958 | 6.0 | 8238 | 0.4662 | 0.9018 | | |
| | 0.4821 | 7.0 | 9611 | 0.4561 | 0.9035 | | |
| | 0.469 | 8.0 | 10984 | 0.4625 | 0.9034 | | |
| | 0.4718 | 9.0 | 12357 | 0.4522 | 0.9048 | | |
| | 0.4642 | 10.0 | 13730 | 0.4526 | 0.9038 | | |
| ### Framework versions | |
| - Transformers 4.25.1 | |
| - Pytorch 1.13.0+cu117 | |
| - Datasets 2.7.1 | |
| - Tokenizers 0.13.2 | |