Text Classification
Transformers
PyTorch
roberta
topic
classification
news
Eval Results (legacy)
text-embeddings-inference
Instructions to use dstefa/roberta-base_topic_classification_nyt_news with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dstefa/roberta-base_topic_classification_nyt_news with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dstefa/roberta-base_topic_classification_nyt_news")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dstefa/roberta-base_topic_classification_nyt_news") model = AutoModelForSequenceClassification.from_pretrained("dstefa/roberta-base_topic_classification_nyt_news", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dstefa/roberta-base_topic_classification_nyt_news: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/dstefa/roberta-base_topic_classification_nyt_news/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dstefa/roberta-base_topic_classification_nyt_news/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dstefa/roberta-base_topic_classification_nyt_news/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 889797882fb0aec01477ef0b854aa52e1cc5b338b62947b5a1eaf2dfabe4b094
- Size of remote file:
- 499 MB
- SHA256:
- d4eee92fbc6cbc4ffa4af88de3ae832177477678172b4b3ce5a5f6570373424c
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