Instructions to use shengqin/bert-seq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shengqin/bert-seq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="shengqin/bert-seq")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("shengqin/bert-seq") model = AutoModelForSequenceClassification.from_pretrained("shengqin/bert-seq", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 24a57d1a14c9d9f3bd3e95f65c9cb73a4fb16d90279429faf91aaaa2fe260340
- Size of remote file:
- 711 MB
- SHA256:
- ec3402e10d7b615f548f6c7680e976c956da1367dc9811d5a90b40723ed1997d
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