Text Classification
Transformers
PyTorch
TensorFlow
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use TirkNork/sentence_classfication_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TirkNork/sentence_classfication_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TirkNork/sentence_classfication_test")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TirkNork/sentence_classfication_test") model = AutoModelForSequenceClassification.from_pretrained("TirkNork/sentence_classfication_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from TirkNork/sentence_classfication_test: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/TirkNork/sentence_classfication_test/resolve/main/tf_model.h5
- Command line
-
hf download hf://TirkNork/sentence_classfication_test/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/TirkNork/sentence_classfication_test/resolve/main/tf_model.h5
268 MB
- Xet hash:
- 70aed04975f8ac6f2f54adc9a97541359fceb843ed051a318431181d1f47e577
- Size of remote file:
- 268 MB
- SHA256:
- e0a3679a3067180ebecfcb831ef6bbb4e7133ebe06f63125657bb02f254d6908
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