Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
Instructions to use runningsnake/bert-base-sequence-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use runningsnake/bert-base-sequence-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="runningsnake/bert-base-sequence-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("runningsnake/bert-base-sequence-classification") model = AutoModelForSequenceClassification.from_pretrained("runningsnake/bert-base-sequence-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from runningsnake/bert-base-sequence-classification: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/runningsnake/bert-base-sequence-classification/resolve/main/tf_model.h5
- Command line
-
hf download hf://runningsnake/bert-base-sequence-classification/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/runningsnake/bert-base-sequence-classification/resolve/main/tf_model.h5
438 MB
- Xet hash:
- de416391dd943318cfc7122d9086a002abda71a1afb9ee683e34ccff68921f35
- Size of remote file:
- 438 MB
- SHA256:
- 808c303d1b911135200fcccefe68adc8baa68501f7f6ddf792953a352b2887e7
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