Text Classification
Transformers
PyTorch
Safetensors
English
distilbert
text Classification
text-embeddings-inference
Instructions to use Sakil/IMDB_URDUSENTIMENT_MODEL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sakil/IMDB_URDUSENTIMENT_MODEL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sakil/IMDB_URDUSENTIMENT_MODEL")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sakil/IMDB_URDUSENTIMENT_MODEL") model = AutoModelForSequenceClassification.from_pretrained("Sakil/IMDB_URDUSENTIMENT_MODEL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Sakil/IMDB_URDUSENTIMENT_MODEL: direct link, hf CLI and curl.
- Browser
- Download file 711 kB
-
https://huggingface.co/Sakil/IMDB_URDUSENTIMENT_MODEL/resolve/fb26794612d980917e7d8610d21bdb8a5a5e2eea/tokenizer.json
- Command line
-
hf download hf://Sakil/IMDB_URDUSENTIMENT_MODEL@fb26794612d980917e7d8610d21bdb8a5a5e2eea/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/Sakil/IMDB_URDUSENTIMENT_MODEL/resolve/fb26794612d980917e7d8610d21bdb8a5a5e2eea/tokenizer.json
711 kB
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