Text Classification
Transformers
PyTorch
English
roberta
RoBERTa-large
topic
news
text-embeddings-inference
Instructions to use dell-research-harvard/topic-antitrust with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dell-research-harvard/topic-antitrust with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dell-research-harvard/topic-antitrust")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dell-research-harvard/topic-antitrust") model = AutoModelForSequenceClassification.from_pretrained("dell-research-harvard/topic-antitrust") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- af4883d4b5eda65ce88371cf1483e3169a2c1ea0ffa86dbb15454af1c159c0ae
- Size of remote file:
- 1.42 GB
- SHA256:
- 9b1481fe5f0ba9fa4fab168faaed9df398da9b41b954bbda19a1e645c150740f
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