Instructions to use hf-internal-testing/tiny-random-BertForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-BertForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-BertForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-BertForSequenceClassification") - Inference
- Notebooks
- Google Colab
- Kaggle
[Awaiting approval] Upload ONNX weights
Browse files[Automated] Converted using [Optimum](https://github.com/huggingface/optimum). Models will be merged manually by @Xenova once they have been checked with [Transformers.js](https://github.com/xenova/transformers.js).
- onnx/model.onnx +3 -0
onnx/model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:65bc056b84329f1847db4402776582d429761e44a307e15f42b973d39084ef63
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size 464127
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