Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use ajrayman/Openness_continuous with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ajrayman/Openness_continuous with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ajrayman/Openness_continuous")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ajrayman/Openness_continuous") model = AutoModelForSequenceClassification.from_pretrained("ajrayman/Openness_continuous") - Notebooks
- Google Colab
- Kaggle
Openness_continuous
This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1422
- Rmse: 0.3770
- Mae: 0.3228
- Corr: 0.1047
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: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Mae | Corr |
|---|---|---|---|---|---|---|
| No log | 1.0 | 1 | 0.1990 | 0.4461 | 0.3894 | -0.2383 |
| No log | 2.0 | 2 | 0.1598 | 0.3998 | 0.3456 | 0.0521 |
| No log | 3.0 | 3 | 0.1422 | 0.3770 | 0.3228 | 0.1047 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.4.0
- Datasets 2.20.0
- Tokenizers 0.21.1
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Model tree for ajrayman/Openness_continuous
Base model
FacebookAI/roberta-base