Text Classification
Transformers
TensorBoard
Safetensors
Persian
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use papooabedini/testing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use papooabedini/testing with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="papooabedini/testing")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("papooabedini/testing") model = AutoModelForSequenceClassification.from_pretrained("papooabedini/testing", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| tags: | |
| - autotrain | |
| - text-classification | |
| base_model: HooshvareLab/bert-fa-base-uncased | |
| widget: | |
| - text: I love AutoTrain | |
| language: | |
| - fa | |
| pipeline_tag: text-classification | |
| # Model Trained Using AutoTrain | |
| - Problem type: Text Classification | |
| ## Validation Metrics | |
| loss: 0.07203829288482666 | |
| f1_macro: 0.988301685943485 | |
| f1_micro: 0.9868173258003766 | |
| f1_weighted: 0.9867815660378358 | |
| precision_macro: 0.9884888715707831 | |
| precision_micro: 0.9868173258003766 | |
| precision_weighted: 0.9869930752212238 | |
| recall_macro: 0.9883333333333333 | |
| recall_micro: 0.9868173258003766 | |
| recall_weighted: 0.9868173258003766 | |
| accuracy: 0.9868173258003766 |