Text Classification
Transformers
TensorBoard
Safetensors
deberta-v2
Trained with AutoTrain
text-embeddings-inference
Instructions to use idobn/twitter-mbti-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use idobn/twitter-mbti-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="idobn/twitter-mbti-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("idobn/twitter-mbti-v2") model = AutoModelForSequenceClassification.from_pretrained("idobn/twitter-mbti-v2") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| tags: | |
| - autotrain | |
| - text-classification | |
| base_model: microsoft/deberta-v3-large | |
| widget: | |
| - text: "I love AutoTrain" | |
| # Model Trained Using AutoTrain | |
| - Problem type: Text Classification | |
| ## Validation Metrics | |
| loss: 1.9677170515060425 | |
| f1_macro: 0.43647747790260216 | |
| f1_micro: 0.40711847879083374 | |
| f1_weighted: 0.3874051890698862 | |
| precision_macro: 0.49034231056721467 | |
| precision_micro: 0.40711847879083374 | |
| precision_weighted: 0.4284233711977137 | |
| recall_macro: 0.4407702395816866 | |
| recall_micro: 0.40711847879083374 | |
| recall_weighted: 0.40711847879083374 | |
| accuracy: 0.40711847879083374 | |