Instructions to use himanshubeniwal/bert_sst_ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use himanshubeniwal/bert_sst_ft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="himanshubeniwal/bert_sst_ft")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("himanshubeniwal/bert_sst_ft") model = AutoModelForSequenceClassification.from_pretrained("himanshubeniwal/bert_sst_ft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9985eaf1b0bd651f5708277b6a40acc8c24b306d572cc4dc3d70c64b4b0e1d1a
- Size of remote file:
- 438 MB
- SHA256:
- 32ebf7d6e05d5c999ef5e8a0a53858d340db2c439d76ad4829aeda021981629d
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