Text Classification
Transformers
PyTorch
TensorBoard
English
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use lukxus/GoEmotions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lukxus/GoEmotions with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lukxus/GoEmotions")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lukxus/GoEmotions") model = AutoModelForSequenceClassification.from_pretrained("lukxus/GoEmotions", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from lukxus/GoEmotions: direct link, hf CLI and curl.
- Browser
- Download file 901 Bytes
-
https://huggingface.co/lukxus/GoEmotions/resolve/main/README.md
- Command line
-
hf download hf://lukxus/GoEmotions/README.md
-
curl -L -H "Authorization: Bearer $HF_TOKEN" -o README.md https://huggingface.co/lukxus/GoEmotions/resolve/main/README.md
901 Bytes
metadata
datasets:
- antoniomenezes/go_emotions_ptbr
language:
- en
metrics:
- f1
tags:
- generated_from_trainer
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-5
- 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: 3
- mixed_precision_training: Native AMP
- warmup_ratio: 0.1
- weight_decay=1e-2
Training results
| Training Loss | Epoch | Validation Loss | F1 | F1 Macro |
|---|---|---|---|---|
| 1.238700 | 1.0 | 1.135360 | 0.238667 | 0.106812 |
| 1.084500 | 2.0 | 1.103713 | 0.234572 | 0.105525 |
| 1.009300 | 3.0 | 1.099275 | 0.290353 | 0.162856 |
| 0.929300 | 4.0 | 1.159663 | 0.334926 | 0.208453 |