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 pytorch_model.bin from palakagl/bert_TextClassification: direct link, hf CLI and curl.
- Browser
- Download file 434 MB
-
https://huggingface.co/palakagl/bert_TextClassification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://palakagl/bert_TextClassification/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/palakagl/bert_TextClassification/resolve/main/pytorch_model.bin
434 MB
- Xet hash:
- 4738e0845b81267bb1e2f7b8b6154f57d0317323429131f796a10e421004dcfd
- Size of remote file:
- 434 MB
- SHA256:
- 41b27d733cd64e595ca76ad9f7725c7dc2d6ee1b020113d84cb1ceb60cd03621
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