Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use ajrayman/Immoderation_continuous with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ajrayman/Immoderation_continuous with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ajrayman/Immoderation_continuous")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ajrayman/Immoderation_continuous") model = AutoModelForSequenceClassification.from_pretrained("ajrayman/Immoderation_continuous", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Immoderation_continuous
This model is a fine-tuned version of microsoft/deberta-v3-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0478
- Rmse: 0.2187
- Mae: 0.1759
- Corr: 0.1721
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:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 1234
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Mae | Corr |
|---|---|---|---|---|---|---|
| No log | 1.0 | 235 | 0.0414 | 0.2034 | 0.1611 | 0.2073 |
| No log | 2.0 | 470 | 0.0407 | 0.2018 | 0.1604 | 0.2359 |
| 0.0553 | 3.0 | 705 | 0.0437 | 0.2091 | 0.1668 | 0.2044 |
| 0.0553 | 4.0 | 940 | 0.0478 | 0.2187 | 0.1759 | 0.1721 |
Framework versions
- Transformers 4.44.1
- Pytorch 1.11.0
- Datasets 2.12.0
- Tokenizers 0.19.1
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Model tree for ajrayman/Immoderation_continuous
Base model
microsoft/deberta-v3-base