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 training_args.bin from dang1812/trained_model: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/dang1812/trained_model/resolve/main/training_args.bin
- Command line
-
hf download hf://dang1812/trained_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dang1812/trained_model/resolve/main/training_args.bin
3.58 kB
- Xet hash:
- 8bb8ff8d65e1a16a27710df6af33270ddde90688192a39fa4dbed06bf383b4ef
- Size of remote file:
- 3.58 kB
- SHA256:
- 3b9f7b384b19e41ffdaeda07afd1ffec66a788671efbaefd934a36e6465c7b4a
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