Instructions to use hf-internal-testing/tiny-random-LayoutLMForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LayoutLMForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-LayoutLMForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-LayoutLMForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-LayoutLMForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0a37f13f37ab77291886f62ebc1277a0a961300d0041dbc17a614f9284165846
- Size of remote file:
- 891 kB
- SHA256:
- aea663547c488b4cc46784d24d3e73103551a7748d56e465b5a4a1486b691ac4
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