Instructions to use deepesh0x/bert_wikipedia_sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepesh0x/bert_wikipedia_sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="deepesh0x/bert_wikipedia_sst2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("deepesh0x/bert_wikipedia_sst2") model = AutoModelForSequenceClassification.from_pretrained("deepesh0x/bert_wikipedia_sst2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download sample_input.pkl from deepesh0x/bert_wikipedia_sst2: direct link, hf CLI and curl.
- Browser
- Download file 3.62 kB
-
https://huggingface.co/deepesh0x/bert_wikipedia_sst2/resolve/refs%2Fpr%2F1/sample_input.pkl
- Command line
-
hf download hf://deepesh0x/bert_wikipedia_sst2@refs/pr/1/sample_input.pkl
-
curl -L -o sample_input.pkl https://huggingface.co/deepesh0x/bert_wikipedia_sst2/resolve/refs%2Fpr%2F1/sample_input.pkl
3.62 kB
- Xet hash:
- d2a2934beeca1d4dc5a119221ca23e9b410d1da7cbbadccc6cfbe4a187b2e60f
- Size of remote file:
- 3.62 kB
- SHA256:
- de1e0e8123d58e3928f79a00ac6a203d6e7d7ff24df0e7d49434f5164de172f1
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