Text Classification
Transformers
TensorFlow
roberta
generated_from_keras_callback
text-embeddings-inference
Instructions to use smrrazavian/comment-classificationr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smrrazavian/comment-classificationr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="smrrazavian/comment-classificationr")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("smrrazavian/comment-classificationr") model = AutoModelForSequenceClassification.from_pretrained("smrrazavian/comment-classificationr") - Notebooks
- Google Colab
- Kaggle
smrrazavian/comment-classificationr
This model is a fine-tuned version of HooshvareLab/roberta-fa-zwnj-base on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.1610
- Train Accuracy: 0.9375
- Epoch: 4
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 2e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Train Accuracy | Epoch |
|---|---|---|
| 0.4729 | 0.7795 | 0 |
| 0.4051 | 0.8176 | 1 |
| 0.3309 | 0.8576 | 2 |
| 0.2352 | 0.9043 | 3 |
| 0.1610 | 0.9375 | 4 |
Framework versions
- Transformers 4.34.0
- TensorFlow 2.12.0
- Tokenizers 0.14.1
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Model tree for smrrazavian/comment-classificationr
Base model
HooshvareLab/roberta-fa-zwnj-base