Instructions to use doomnova/distilbert_system_A with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use doomnova/distilbert_system_A with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="doomnova/distilbert_system_A")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("doomnova/distilbert_system_A") model = AutoModelForTokenClassification.from_pretrained("doomnova/distilbert_system_A", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: distilbert-base-cased | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - precision | |
| - recall | |
| - f1 | |
| - accuracy | |
| model-index: | |
| - name: distilbert_system_A | |
| results: [] | |
| datasets: | |
| - Babelscape/multinerd | |
| language: | |
| - en | |
| <!-- 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. --> | |
| # distilbert_system_A | |
| This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.0547 | |
| - Precision: 0.8996 | |
| - Recall: 0.9132 | |
| - F1: 0.9063 | |
| - Accuracy: 0.9850 | |
| ## 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: 32 | |
| - eval_batch_size: 32 | |
| - 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 | | |
| |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | |
| | 0.0278 | 1.0 | 8205 | 0.0434 | 0.8992 | 0.8977 | 0.8984 | 0.9843 | | |
| | 0.0161 | 2.0 | 16410 | 0.0477 | 0.9067 | 0.9065 | 0.9066 | 0.9851 | | |
| | 0.0097 | 3.0 | 24615 | 0.0547 | 0.8996 | 0.9132 | 0.9063 | 0.9850 | | |
| ### Framework versions | |
| - Transformers 4.35.2 | |
| - Pytorch 2.1.0+cu118 | |
| - Datasets 2.15.0 | |
| - Tokenizers 0.15.0 |