Instructions to use gsl22/ellis-v1-emotion-positive-emotions2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gsl22/ellis-v1-emotion-positive-emotions2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gsl22/ellis-v1-emotion-positive-emotions2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gsl22/ellis-v1-emotion-positive-emotions2") model = AutoModelForSequenceClassification.from_pretrained("gsl22/ellis-v1-emotion-positive-emotions2") - Notebooks
- Google Colab
- Kaggle
ellis-v1-emotion-positive-emotions2
This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5876
- Accuracy: 0.8003
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7042 | 1.0 | 3885 | 0.6515 | 0.7716 |
| 0.6124 | 2.0 | 7770 | 0.5955 | 0.7846 |
| 0.5292 | 3.0 | 11655 | 0.6043 | 0.7944 |
| 0.4543 | 4.0 | 15540 | 0.5876 | 0.8003 |
| 0.3953 | 5.0 | 19425 | 0.6153 | 0.8103 |
| 0.3301 | 6.0 | 23310 | 0.6478 | 0.8100 |
| 0.2737 | 7.0 | 27195 | 0.6919 | 0.8103 |
| 0.2034 | 8.0 | 31080 | 0.7899 | 0.8142 |
| 0.1968 | 9.0 | 34965 | 0.8648 | 0.8151 |
| 0.167 | 10.0 | 38850 | 0.9208 | 0.8154 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.1.0+cu121
- Datasets 2.13.0
- Tokenizers 0.13.3
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Model tree for gsl22/ellis-v1-emotion-positive-emotions2
Base model
FacebookAI/roberta-base