Instructions to use eawang/bert2_tf_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eawang/bert2_tf_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eawang/bert2_tf_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eawang/bert2_tf_model") model = AutoModelForSequenceClassification.from_pretrained("eawang/bert2_tf_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from eawang/bert2_tf_model: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/eawang/bert2_tf_model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://eawang/bert2_tf_model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/eawang/bert2_tf_model/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- de76aae98962c891282607922ef32f656c13cd46db55c505be2e8dc171794df0
- Size of remote file:
- 268 MB
- SHA256:
- e916e1d1baccae7c57b54f9dfd9caab831f286451446a26a7587493cab812c5e
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