Instructions to use jamesLeeeeeee/code-search-net-tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jamesLeeeeeee/code-search-net-tokenizer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jamesLeeeeeee/code-search-net-tokenizer")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jamesLeeeeeee/code-search-net-tokenizer") model = AutoModelForTokenClassification.from_pretrained("jamesLeeeeeee/code-search-net-tokenizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: bert-base-cased | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - precision | |
| - recall | |
| - f1 | |
| - accuracy | |
| model-index: | |
| - name: code-search-net-tokenizer | |
| 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. --> | |
| # code-search-net-tokenizer | |
| This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.0619 | |
| - Precision: 0.9355 | |
| - Recall: 0.9502 | |
| - F1: 0.9428 | |
| - Accuracy: 0.9860 | |
| ## 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 | |
| - num_epochs: 3 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | |
| | 0.0749 | 1.0 | 1756 | 0.0704 | 0.8997 | 0.9303 | 0.9148 | 0.9805 | | |
| | 0.0367 | 2.0 | 3512 | 0.0636 | 0.9378 | 0.9497 | 0.9437 | 0.9858 | | |
| | 0.0243 | 3.0 | 5268 | 0.0619 | 0.9355 | 0.9502 | 0.9428 | 0.9860 | | |
| ### Framework versions | |
| - Transformers 4.38.2 | |
| - Pytorch 2.1.0+cu121 | |
| - Datasets 2.18.0 | |
| - Tokenizers 0.15.2 | |