Instructions to use minh21/XLNet-Toxic-Comment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use minh21/XLNet-Toxic-Comment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="minh21/XLNet-Toxic-Comment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("minh21/XLNet-Toxic-Comment") model = AutoModelForSequenceClassification.from_pretrained("minh21/XLNet-Toxic-Comment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| base_model: xlnet-base-cased | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| - precision | |
| - recall | |
| - f1 | |
| model-index: | |
| - name: XLNet-Toxic-Comment | |
| 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. --> | |
| # XLNet-Toxic-Comment | |
| This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.4431 | |
| - Rmse: 0.3106 | |
| - Accuracy: 0.9035 | |
| - Precision: 0.0 | |
| - Recall: 0.0 | |
| - F1: 0.0 | |
| ## 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: 3e-05 | |
| - train_batch_size: 2 | |
| - eval_batch_size: 2 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 4 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Rmse | Accuracy | Precision | Recall | F1 | | |
| |:-------------:|:-----:|:------:|:---------------:|:------:|:--------:|:---------:|:------:|:---:| | |
| | 0.4641 | 1.0 | 61873 | 0.5115 | 0.3106 | 0.9035 | 0.0 | 0.0 | 0.0 | | |
| | 0.5065 | 2.0 | 123746 | 0.4431 | 0.3106 | 0.9035 | 0.0 | 0.0 | 0.0 | | |
| | 0.5033 | 3.0 | 185619 | 0.4734 | 0.3106 | 0.9035 | 0.0 | 0.0 | 0.0 | | |
| | 0.5004 | 4.0 | 247492 | 0.4710 | 0.3106 | 0.9035 | 0.0 | 0.0 | 0.0 | | |
| ### Framework versions | |
| - Transformers 4.35.0.dev0 | |
| - Pytorch 2.0.0 | |
| - Datasets 2.1.0 | |
| - Tokenizers 0.14.1 | |