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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hf-internal-testing/tiny-random-LayoutLMForSequenceClassification: direct link, hf CLI and curl.
- Browser
- Download file 891 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-LayoutLMForSequenceClassification/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://hf-internal-testing/tiny-random-LayoutLMForSequenceClassification@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-internal-testing/tiny-random-LayoutLMForSequenceClassification/resolve/refs%2Fpr%2F1/model.safetensors
891 kB
- Xet hash:
- 0a37f13f37ab77291886f62ebc1277a0a961300d0041dbc17a614f9284165846
- Size of remote file:
- 891 kB
- SHA256:
- aea663547c488b4cc46784d24d3e73103551a7748d56e465b5a4a1486b691ac4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.