Instructions to use ongknsro/ACARIS-DistilBERT_MLPUserEmbs-iter1-batchSize32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ongknsro/ACARIS-DistilBERT_MLPUserEmbs-iter1-batchSize32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ongknsro/ACARIS-DistilBERT_MLPUserEmbs-iter1-batchSize32")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, DistilBertForMulticlassSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ongknsro/ACARIS-DistilBERT_MLPUserEmbs-iter1-batchSize32") model = DistilBertForMulticlassSequenceClassification.from_pretrained("ongknsro/ACARIS-DistilBERT_MLPUserEmbs-iter1-batchSize32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ongknsro/ACARIS-DistilBERT_MLPUserEmbs-iter1-batchSize32: direct link, hf CLI and curl.
- Browser
- Download file 275 MB
-
https://huggingface.co/ongknsro/ACARIS-DistilBERT_MLPUserEmbs-iter1-batchSize32/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ongknsro/ACARIS-DistilBERT_MLPUserEmbs-iter1-batchSize32/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ongknsro/ACARIS-DistilBERT_MLPUserEmbs-iter1-batchSize32/resolve/main/pytorch_model.bin
275 MB
- Xet hash:
- 65bd5540141879b29987490782233aadd8862b44fc7a3bfe739d94c6ea8dc8e4
- Size of remote file:
- 275 MB
- SHA256:
- 10d31b01c0cab63d0582bf4f968b926379bd26270f432f67cb1d452c9e351e5c
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