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
File size: 322 Bytes
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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. |