Text Classification
Transformers
Safetensors
English
qwen3
emotion-detection
text-embeddings-inference
4-bit precision
bitsandbytes
Instructions to use agk4444/aditya369 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use agk4444/aditya369 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="agk4444/aditya369")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("agk4444/aditya369") model = AutoModelForSequenceClassification.from_pretrained("agk4444/aditya369", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from agk4444/aditya369: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/agk4444/aditya369/resolve/main/tokenizer.json
- Command line
-
hf download hf://agk4444/aditya369/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/agk4444/aditya369/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 4b15557d92834f03ff0b0cac85f8b73e3128295ce8ec9e4c94421c1246409b40
- Size of remote file:
- 11.4 MB
- SHA256:
- af695a1f02f57bc392b55dda26d1393eeb4f83c85527d5852990f088bfc8ad8e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.