Text Classification
Transformers
PyTorch
English
roberta
distilroberta
topic
news
text-embeddings-inference
Instructions to use dell-research-harvard/topic-fire with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dell-research-harvard/topic-fire with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dell-research-harvard/topic-fire")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dell-research-harvard/topic-fire") model = AutoModelForSequenceClassification.from_pretrained("dell-research-harvard/topic-fire", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dell-research-harvard/topic-fire: direct link, hf CLI and curl.
- Browser
- Download file 329 MB
-
https://huggingface.co/dell-research-harvard/topic-fire/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dell-research-harvard/topic-fire/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dell-research-harvard/topic-fire/resolve/main/pytorch_model.bin
329 MB
- Xet hash:
- c75a4fc463a0bd6721d84dd9b6e01da1f5eba54591f62bc15cfc3d4940c057a0
- Size of remote file:
- 329 MB
- SHA256:
- be6f5abb1d321acb555d337514f887618a7f164913efb24c1a8c7ca64d349897
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