Instructions to use pmthangk09/bert-base-uncased-glue-sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pmthangk09/bert-base-uncased-glue-sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pmthangk09/bert-base-uncased-glue-sst2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pmthangk09/bert-base-uncased-glue-sst2") model = AutoModelForSequenceClassification.from_pretrained("pmthangk09/bert-base-uncased-glue-sst2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8eaa4b5f7740fd910f0d3f92c181d3c68f844f6bd175c55b4adf333aa88bd105
- Size of remote file:
- 438 MB
- SHA256:
- 48e158d3483cb4afdec2a0eaea935478c486d89d346b5beb49cdadc58507d776
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