Text Classification
Transformers
PyTorch
English
bert
negation
evaluation
metric
text-embeddings-inference
Instructions to use tum-nlp/NegBLEURT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tum-nlp/NegBLEURT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tum-nlp/NegBLEURT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tum-nlp/NegBLEURT") model = AutoModelForSequenceClassification.from_pretrained("tum-nlp/NegBLEURT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tum-nlp/NegBLEURT: direct link, hf CLI and curl.
- Browser
- Download file 17.6 MB
-
https://huggingface.co/tum-nlp/NegBLEURT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tum-nlp/NegBLEURT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tum-nlp/NegBLEURT/resolve/main/pytorch_model.bin
17.6 MB
- Xet hash:
- 1459ea47c92e388f2689e009794376c719440084c4270256fc404a70acfba6ad
- Size of remote file:
- 17.6 MB
- SHA256:
- ba9e62eb29b448b8fb5a429400144e5f6bdc8b976aa8129f8ce0e1177fc07c46
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.