Instructions to use Unggi/hate_speech_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Unggi/hate_speech_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Unggi/hate_speech_bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Unggi/hate_speech_bert") model = AutoModelForSequenceClassification.from_pretrained("Unggi/hate_speech_bert") - Notebooks
- Google Colab
- Kaggle
Upload tokenizer
Browse files- tokenizer_config.json +0 -1
tokenizer_config.json
CHANGED
|
@@ -4,7 +4,6 @@
|
|
| 4 |
"do_lower_case": true,
|
| 5 |
"mask_token": "[MASK]",
|
| 6 |
"model_max_length": 1000000000000000019884624838656,
|
| 7 |
-
"name_or_path": "beomi/kobert",
|
| 8 |
"never_split": null,
|
| 9 |
"pad_token": "[PAD]",
|
| 10 |
"sep_token": "[SEP]",
|
|
|
|
| 4 |
"do_lower_case": true,
|
| 5 |
"mask_token": "[MASK]",
|
| 6 |
"model_max_length": 1000000000000000019884624838656,
|
|
|
|
| 7 |
"never_split": null,
|
| 8 |
"pad_token": "[PAD]",
|
| 9 |
"sep_token": "[SEP]",
|