Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use JingLang/fine_tuned_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JingLang/fine_tuned_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JingLang/fine_tuned_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JingLang/fine_tuned_model") model = AutoModelForSequenceClassification.from_pretrained("JingLang/fine_tuned_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from JingLang/fine_tuned_model: direct link, hf CLI and curl.
- Browser
- Download file 5.27 kB
-
https://huggingface.co/JingLang/fine_tuned_model/resolve/main/training_args.bin
- Command line
-
hf download hf://JingLang/fine_tuned_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/JingLang/fine_tuned_model/resolve/main/training_args.bin
5.27 kB
- Xet hash:
- 1620d27bf4ff292331dd8b01f16f67b5030cba18ffd75d1ceed93897afa98731
- Size of remote file:
- 5.27 kB
- SHA256:
- 4117712ee7552415106999f3b7a264f1339409a16d3c09fd542cb46a03b803a6
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