Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use AFZALS/ToxicClassification2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AFZALS/ToxicClassification2.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AFZALS/ToxicClassification2.0")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AFZALS/ToxicClassification2.0") model = AutoModelForSequenceClassification.from_pretrained("AFZALS/ToxicClassification2.0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b025c9e15ea69c133a69702d47d71c4fd323b46cedff7757d0797d977159c606
- Size of remote file:
- 1.11 GB
- SHA256:
- 588c9c0e0b0d2699e0efca08939e2bf0984c546225c21af55a68794b24717f1b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.