Instructions to use palakagl/bert_TextClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use palakagl/bert_TextClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="palakagl/bert_TextClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("palakagl/bert_TextClassification") model = AutoModelForSequenceClassification.from_pretrained("palakagl/bert_TextClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download sample_input.pkl from palakagl/bert_TextClassification: direct link, hf CLI and curl.
- Browser
- Download file 4.38 kB
-
https://huggingface.co/palakagl/bert_TextClassification/resolve/main/sample_input.pkl
- Command line
-
hf download hf://palakagl/bert_TextClassification/sample_input.pkl
-
curl -L -o sample_input.pkl https://huggingface.co/palakagl/bert_TextClassification/resolve/main/sample_input.pkl
4.38 kB
- Xet hash:
- e3448c9e44f12140c2f3a17ba6ad3f2da5d2a9309b40d83a2aa5a78ed5339248
- Size of remote file:
- 4.38 kB
- SHA256:
- bfb02be1a26f41b446ef11401f8ce316ca9ac8e659425ee377e26e4bb493c092
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