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 task_metadata.json from JingLang/fine_tuned_model: direct link, hf CLI and curl.
- Browser
- Download file 150 Bytes
-
https://huggingface.co/JingLang/fine_tuned_model/resolve/main/task_metadata.json
- Command line
-
hf download hf://JingLang/fine_tuned_model/task_metadata.json
-
curl -L -o task_metadata.json https://huggingface.co/JingLang/fine_tuned_model/resolve/main/task_metadata.json
150 Bytes
| { | |
| "candidate_labels": [ | |
| "FACT", | |
| "QUESTION", | |
| "REQUEST", | |
| "OPINION", | |
| "COMPLAINT" | |
| ], | |
| "hypothesis_template": "This text is {}." | |
| } |