Instructions to use davidgaofc/hh-labeler with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use davidgaofc/hh-labeler with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="davidgaofc/hh-labeler")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("davidgaofc/hh-labeler") model = AutoModelForSequenceClassification.from_pretrained("davidgaofc/hh-labeler", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 380611cb7d5d4cc9f02b0adc885618a4fc85a00bcb1e72c25dbeec82d72fbd8f
- Size of remote file:
- 4.54 kB
- SHA256:
- 0593195cb205c948b236a4ea4fec00d9ff6faf50764553a49a80c718ff60d1e5
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