Instructions to use eawang/bert_tf_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eawang/bert_tf_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eawang/bert_tf_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eawang/bert_tf_model") model = AutoModelForSequenceClassification.from_pretrained("eawang/bert_tf_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from eawang/bert_tf_model: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/eawang/bert_tf_model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://eawang/bert_tf_model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/eawang/bert_tf_model/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- fa1e7265a8efebf45ba1df576711c417fe033614984f7502be4d3dc5d1baa3e4
- Size of remote file:
- 268 MB
- SHA256:
- f93e6b64d2d5af480c010f4d8be60db73d2e4e305be7de32dd3bf055e5b604bb
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