Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use marcolatella/Hps_seed1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use marcolatella/Hps_seed1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="marcolatella/Hps_seed1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("marcolatella/Hps_seed1") model = AutoModelForSequenceClassification.from_pretrained("marcolatella/Hps_seed1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from marcolatella/Hps_seed1: direct link, hf CLI and curl.
- Browser
- Download file 2.93 kB
-
https://huggingface.co/marcolatella/Hps_seed1/resolve/main/training_args.bin
- Command line
-
hf download hf://marcolatella/Hps_seed1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/marcolatella/Hps_seed1/resolve/main/training_args.bin
2.93 kB
- Xet hash:
- 951049e4e7bf181f7eaf0a455276ec71b70f241daa6ae6bb02c40fcb228154e2
- Size of remote file:
- 2.93 kB
- SHA256:
- cda231377268e6175f7ad16f700c7e93cc701e542301f5dcd246010a82683aae
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.