Instructions to use ydshieh/tiny-random-BertForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ydshieh/tiny-random-BertForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ydshieh/tiny-random-BertForSequenceClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ydshieh/tiny-random-BertForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("ydshieh/tiny-random-BertForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from ydshieh/tiny-random-BertForSequenceClassification: direct link, hf CLI and curl.
- Browser
- Download file 481 kB
-
https://huggingface.co/ydshieh/tiny-random-BertForSequenceClassification/resolve/refs%2Fpr%2F1/tf_model.h5
- Command line
-
hf download hf://ydshieh/tiny-random-BertForSequenceClassification@refs/pr/1/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/ydshieh/tiny-random-BertForSequenceClassification/resolve/refs%2Fpr%2F1/tf_model.h5
481 kB
- Xet hash:
- afe14bddb5a1ec8cf27d1643cc1bdd87fa58871162fa7d3ed29c18438059c733
- Size of remote file:
- 481 kB
- SHA256:
- 6c57d60ffbc8cbe72119da9f75d32f538b6037749b42b932ea0c47a0d1192959
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