Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use marcolatella/hate_trained_42 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use marcolatella/hate_trained_42 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="marcolatella/hate_trained_42")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("marcolatella/hate_trained_42") model = AutoModelForSequenceClassification.from_pretrained("marcolatella/hate_trained_42", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from marcolatella/hate_trained_42: direct link, hf CLI and curl.
- Browser
- Download file 2.93 kB
-
https://huggingface.co/marcolatella/hate_trained_42/resolve/main/training_args.bin
- Command line
-
hf download hf://marcolatella/hate_trained_42/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/marcolatella/hate_trained_42/resolve/main/training_args.bin
2.93 kB
- Xet hash:
- 3ed01d7ad6b07b19b0316f34265eb6f49e30655ac91a24d42b3e4954d009b29d
- Size of remote file:
- 2.93 kB
- SHA256:
- fe1a54b0f83a135238d74a149277c48993a833fd7a8ba5fd7c323684bf6e8e8c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.