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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tum-nlp/NegBLEURT") model = AutoModelForSequenceClassification.from_pretrained("tum-nlp/NegBLEURT") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#2
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:1025eea8ce23c643e205a1b6fe74ce7892ffe169071cf173e690d21d7c4a3840
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size 17552980
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