Text Classification
Transformers
Safetensors
bert
politics
twitter
tweets
issues
text-embeddings-inference
Instructions to use z-dickson/issue_classification_tweets with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use z-dickson/issue_classification_tweets with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="z-dickson/issue_classification_tweets")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("z-dickson/issue_classification_tweets") model = AutoModelForSequenceClassification.from_pretrained("z-dickson/issue_classification_tweets") - Notebooks
- Google Colab
- Kaggle
analysis
#1
by teejay123 - opened
hi, I'm a bit new here. The models you've made are very interesting! Aside from the code and README attachments, do you have an analysis written up on this model or anything that you can share?
Hi - I have some more details on the specific tasks the models were trained to accomplish in the corresponding journal articles. They’re available on my website - https://z-dickson.github.io/research.html. The article using the above model for tweets is forthcoming but a draft is available here: https://z-dickson.github.io/assets/dickson_PoP_accepted.pdf.
Let me know if you have any questions!
z-dickson changed discussion status to closed