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 training_args.bin from eawang/bert2_tf_model: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/eawang/bert2_tf_model/resolve/main/training_args.bin
- Command line
-
hf download hf://eawang/bert2_tf_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/eawang/bert2_tf_model/resolve/main/training_args.bin
3.58 kB
- Xet hash:
- 9a3b670176a0db4d53ebaa66c0e1cdfa80703e2d857fe2cd62e03c4816411efe
- Size of remote file:
- 3.58 kB
- SHA256:
- 721813da46acb22f4892b926f8f0be228bdb76aa6e36f3080b2fa1e2984a5b7f
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