Text Classification
Transformers
PyTorch
Safetensors
English
roberta
argument-mining
opinion-mining
information-extraction
inference-extraction
Twitter
Instructions to use TomatenMarc/WRAP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TomatenMarc/WRAP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TomatenMarc/WRAP")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TomatenMarc/WRAP") model = AutoModelForSequenceClassification.from_pretrained("TomatenMarc/WRAP", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from TomatenMarc/WRAP: direct link, hf CLI and curl.
- Browser
- Download file 540 MB
-
https://huggingface.co/TomatenMarc/WRAP/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://TomatenMarc/WRAP/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/TomatenMarc/WRAP/resolve/main/pytorch_model.bin
540 MB
- Xet hash:
- 0578e8fb9088da4243f302465c26c5c61326a1c8ce54d0ff25c206e90bd1f9c5
- Size of remote file:
- 540 MB
- SHA256:
- 7dd2b637c9e39effd7913d261cccbff28bc84289ad16e632b4b86ee439c3efc2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.