Text Classification
Transformers
PyTorch
English
sentiment-analysis
emotion-classification
multi-label-classification
deberta-v3
nlp
Instructions to use Praphull1/Sentimental-Analyzer-V3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Praphull1/Sentimental-Analyzer-V3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Praphull1/Sentimental-Analyzer-V3")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Praphull1/Sentimental-Analyzer-V3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model/best_v3_model.pt from Praphull1/Sentimental-Analyzer-V3: direct link, hf CLI and curl.
- Browser
- Download file 2.22 GB
-
https://huggingface.co/Praphull1/Sentimental-Analyzer-V3/resolve/main/model/best_v3_model.pt
- Command line
-
hf download hf://Praphull1/Sentimental-Analyzer-V3/model/best_v3_model.pt
-
curl -L -o best_v3_model.pt https://huggingface.co/Praphull1/Sentimental-Analyzer-V3/resolve/main/model/best_v3_model.pt
2.22 GB
- Xet hash:
- f09dd4a220902ef76683087d834a0f790a9df30b215f26d8010aba957f1a1878
- Size of remote file:
- 2.22 GB
- SHA256:
- 4d3f5a97d352460d41fa884fd337b2de6f58549139135cad1b4b79beefcb2078
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