Text Classification
Transformers
PyTorch
Korean
xlm-roberta
XLM-RoBERTa
KorFin-ASC
financial-sentiment-analysis
sentiment-analysis
text-embeddings-inference
Instructions to use amphora/KorFinASC-XLM-RoBERTa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amphora/KorFinASC-XLM-RoBERTa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="amphora/KorFinASC-XLM-RoBERTa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("amphora/KorFinASC-XLM-RoBERTa") model = AutoModelForSequenceClassification.from_pretrained("amphora/KorFinASC-XLM-RoBERTa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from amphora/KorFinASC-XLM-RoBERTa: direct link, hf CLI and curl.
- Browser
- Download file 2.24 GB
-
https://huggingface.co/amphora/KorFinASC-XLM-RoBERTa/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://amphora/KorFinASC-XLM-RoBERTa/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/amphora/KorFinASC-XLM-RoBERTa/resolve/main/pytorch_model.bin
2.24 GB
- Xet hash:
- 52d94b12a90d5188a74f2b76a1126f0dfd25e5f3cf10bcd386184ff08c6a671c
- Size of remote file:
- 2.24 GB
- SHA256:
- 337d76174f8df58dc24ca167cfa2ef9440a973735ef0bd715133b0b5ecfbba62
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