Instructions to use Arkash/bert_tf_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Arkash/bert_tf_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Arkash/bert_tf_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Arkash/bert_tf_model") model = AutoModelForSequenceClassification.from_pretrained("Arkash/bert_tf_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Arkash/bert_tf_model: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/Arkash/bert_tf_model/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://Arkash/bert_tf_model@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Arkash/bert_tf_model/resolve/refs%2Fpr%2F1/pytorch_model.bin
268 MB
- Xet hash:
- 31e8b93456abb0b2e17be33a5c44ac655d47c7caedc2fa87d4b60fd0d96f7045
- Size of remote file:
- 268 MB
- SHA256:
- 192c40b65f7a5e825401a053881f58b40e019bc145de92cb93ca23f9f2a9b307
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