Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use dang1812/trained_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dang1812/trained_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dang1812/trained_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dang1812/trained_model") model = AutoModelForSequenceClassification.from_pretrained("dang1812/trained_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dang1812/trained_model: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/dang1812/trained_model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dang1812/trained_model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dang1812/trained_model/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- b2f3167d458d0804a35752671c77d53cc2cdb7bc90c8e9acf7c6016cbdcd5a85
- Size of remote file:
- 268 MB
- SHA256:
- 28371afa145f5cc8b4ef57996e5e093139c3c3497d613bf4ae12a1a7205cb378
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