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 pytorch_model.bin from Sakil/IMDB_URDUSENTIMENT_MODEL: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/Sakil/IMDB_URDUSENTIMENT_MODEL/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Sakil/IMDB_URDUSENTIMENT_MODEL/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Sakil/IMDB_URDUSENTIMENT_MODEL/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 704c5975dc62e5abf2fe91d1de4b6543dd807c3070ea6a36923a72e07398d31a
- Size of remote file:
- 268 MB
- SHA256:
- 32895e6a6a723572dbc9c5c8ac5caa682af812f97d5ecc3c76e2349c21633886
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