Instructions to use Johnade/distilbert-base-multilingual-cased-twitter-identity-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Johnade/distilbert-base-multilingual-cased-twitter-identity-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Johnade/distilbert-base-multilingual-cased-twitter-identity-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Johnade/distilbert-base-multilingual-cased-twitter-identity-classification") model = AutoModelForSequenceClassification.from_pretrained("Johnade/distilbert-base-multilingual-cased-twitter-identity-classification") - Notebooks
- Google Colab
- Kaggle
Model Description
This project aims to build a model capable of predicting the identity of a twitter user from their twitter.
Before proceeding I would raise the minor caveat that identity here is defined as male, female, or a brand. In some of the code snippets gender is used in place of identity because of how the column was labelled in the data.
Output Key
Label_0 = Brand
Label_1 = Female
Label_2 = Male
Base model
Base model is the mult distilbert-base-multilingual-cased
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