Instructions to use rb05751/toxic_speech_detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rb05751/toxic_speech_detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rb05751/toxic_speech_detector")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rb05751/toxic_speech_detector") model = AutoModelForSequenceClassification.from_pretrained("rb05751/toxic_speech_detector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from rb05751/toxic_speech_detector: direct link, hf CLI and curl.
- Browser
- Download file 1.23 kB
-
https://huggingface.co/rb05751/toxic_speech_detector/resolve/main/README.md
- Command line
-
hf download hf://rb05751/toxic_speech_detector/README.md
-
curl -L -o README.md https://huggingface.co/rb05751/toxic_speech_detector/resolve/main/README.md
1.23 kB
| base_model: facebook/roberta-hate-speech-dynabench-r4-target | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: toxic_speech_detector | |
| 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. --> | |
| # toxic_speech_detector | |
| This model is a fine-tuned version of [facebook/roberta-hate-speech-dynabench-r4-target](https://huggingface.co/facebook/roberta-hate-speech-dynabench-r4-target) on the [s-nlp/en_paradetox_toxicity](https://huggingface.co/datasets/s-nlp/en_paradetox_toxicity) dataset. | |
| ## 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: 128 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 1 | |
| ### Training results | |
| ### Framework versions | |
| - Transformers 4.32.1 | |
| - Pytorch 2.0.1+cu118 | |
| - Datasets 2.14.4 | |
| - Tokenizers 0.13.3 | |