Text Classification
Transformers
Safetensors
Sindhi
xlm-roberta
sentiment-analysis
sindhi
text-embeddings-inference
Instructions to use DanishMahdi/snd_sentiment_analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DanishMahdi/snd_sentiment_analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DanishMahdi/snd_sentiment_analysis")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DanishMahdi/snd_sentiment_analysis") model = AutoModelForSequenceClassification.from_pretrained("DanishMahdi/snd_sentiment_analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from DanishMahdi/snd_sentiment_analysis: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/DanishMahdi/snd_sentiment_analysis/resolve/main/tokenizer.json
- Command line
-
hf download hf://DanishMahdi/snd_sentiment_analysis/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/DanishMahdi/snd_sentiment_analysis/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- a0dee32d7c57f670deeeb136b31fa7d697992721e669c30a5dc0541d29c1e60e
- Size of remote file:
- 17.1 MB
- SHA256:
- b7c3dd928f1e48cb67c2af5f9f7754e20704ed2ea20e7cb0b30ada3a4140532b
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