Instructions to use veriga/tf_disilbert_binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use veriga/tf_disilbert_binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="veriga/tf_disilbert_binary")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("veriga/tf_disilbert_binary") model = AutoModelForSequenceClassification.from_pretrained("veriga/tf_disilbert_binary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from veriga/tf_disilbert_binary: direct link, hf CLI and curl.
- Browser
- Download file 448 MB
-
https://huggingface.co/veriga/tf_disilbert_binary/resolve/main/tf_model.h5
- Command line
-
hf download hf://veriga/tf_disilbert_binary/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/veriga/tf_disilbert_binary/resolve/main/tf_model.h5
448 MB
- Xet hash:
- 40db48334066dca5cfc987867fcc077edf0e8b88b23fd92340e815400fcaa895
- Size of remote file:
- 448 MB
- SHA256:
- e81ae1e0b9510c1ce1fe7c2a39a9b8569ce7b54de8c4aa31da93ba724c1c42b3
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