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
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Download README.md from Sakil/IMDB_URDUSENTIMENT_MODEL: direct link, hf CLI and curl.
- Browser
- Download file 322 Bytes
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https://huggingface.co/Sakil/IMDB_URDUSENTIMENT_MODEL/resolve/main/README.md
- Command line
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hf download hf://Sakil/IMDB_URDUSENTIMENT_MODEL/README.md
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curl -L -o README.md https://huggingface.co/Sakil/IMDB_URDUSENTIMENT_MODEL/resolve/main/README.md
322 Bytes
metadata
language:
- en
tags:
- text Classification
license: apache-2.0
widget:
- text: میں تمہیں پسند کرتا ہوں. </s></s> میں تم سے پیار کرتا ہوں.
- IMDB_URDUSENTIMENT_MODEL I have used IMDB URDU dataset to create custom model by using DistilBertForSequenceClassification.