Text Classification
Transformers
TensorFlow
roberta
generated_from_keras_callback
text-embeddings-inference
Instructions to use ViditRaj/ROBERTA_Ads_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ViditRaj/ROBERTA_Ads_Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ViditRaj/ROBERTA_Ads_Classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ViditRaj/ROBERTA_Ads_Classifier") model = AutoModelForSequenceClassification.from_pretrained("ViditRaj/ROBERTA_Ads_Classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,927 Bytes
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license: mit
tags:
- generated_from_keras_callback
model-index:
- name: ViditRaj/ROBERTA_Ads_Classifier
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ViditRaj/ROBERTA_Ads_Classifier
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0420
- Validation Loss: 0.2158
- Train Accuracy: 0.9348
- 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': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 465, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.3509 | 0.1830 | 0.9334 | 0 |
| 0.1446 | 0.1676 | 0.9320 | 1 |
| 0.0980 | 0.1691 | 0.9377 | 2 |
| 0.0682 | 0.1938 | 0.9334 | 3 |
| 0.0420 | 0.2158 | 0.9348 | 4 |
### Framework versions
- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|