Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use HyrumR/roberta_train with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HyrumR/roberta_train with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HyrumR/roberta_train")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HyrumR/roberta_train") model = AutoModelForSequenceClassification.from_pretrained("HyrumR/roberta_train", device_map="auto") - Notebooks
- Google Colab
- Kaggle
roberta_train
This model is a fine-tuned version of FacebookAI/roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.8828
- Accuracy: 0.656
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 100 | 1.0986 | 0.575 |
| No log | 2.0 | 200 | 0.9209 | 0.67 |
| No log | 3.0 | 300 | 0.5882 | 0.795 |
| No log | 4.0 | 400 | 0.6371 | 0.765 |
| 0.6746 | 5.0 | 500 | 0.6518 | 0.775 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1
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Model tree for HyrumR/roberta_train
Base model
FacebookAI/roberta-base