Instructions to use Win02/go-emotions-28 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Win02/go-emotions-28 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Win02/go-emotions-28")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Win02/go-emotions-28") model = AutoModelForSequenceClassification.from_pretrained("Win02/go-emotions-28", device_map="auto") - Notebooks
- Google Colab
- Kaggle
go-emotions-28
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.6052
- F1: 0.5091
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: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 1.6017 | 1.0 | 2270 | 1.6172 | 0.4157 |
| 1.5114 | 2.0 | 4540 | 1.4641 | 0.4667 |
| 1.1174 | 3.0 | 6810 | 1.5166 | 0.4654 |
| 0.8679 | 4.0 | 9080 | 1.5674 | 0.5012 |
| 0.7151 | 5.0 | 11350 | 1.6052 | 0.5091 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for Win02/go-emotions-28
Base model
distilbert/distilbert-base-uncased