Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use ericNguyen0132/roberta-large-Dep-first with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ericNguyen0132/roberta-large-Dep-first with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ericNguyen0132/roberta-large-Dep-first")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ericNguyen0132/roberta-large-Dep-first") model = AutoModelForSequenceClassification.from_pretrained("ericNguyen0132/roberta-large-Dep-first") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| - f1 | |
| model-index: | |
| - name: roberta-large-Dep-first | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # roberta-large-Dep-first | |
| This model is a fine-tuned version of [rafalposwiata/deproberta-large-depression](https://huggingface.co/rafalposwiata/deproberta-large-depression) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.1795 | |
| - Accuracy: 0.702 | |
| - F1: 0.5706 | |
| ## 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: 5e-06 | |
| - 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 | F1 | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | |
| | 0.5563 | 1.0 | 751 | 0.5324 | 0.756 | 0.6188 | | |
| | 0.4721 | 2.0 | 1502 | 0.6204 | 0.691 | 0.5874 | | |
| | 0.3836 | 3.0 | 2253 | 0.7990 | 0.696 | 0.525 | | |
| | 0.3245 | 4.0 | 3004 | 0.9714 | 0.694 | 0.5726 | | |
| | 0.2795 | 5.0 | 3755 | 1.1795 | 0.702 | 0.5706 | | |
| ### Framework versions | |
| - Transformers 4.30.2 | |
| - Pytorch 2.0.1+cu118 | |
| - Datasets 2.13.1 | |
| - Tokenizers 0.13.3 | |