Text Classification
Transformers
PyTorch
TensorBoard
English
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use lukxus/TwitterCorona with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lukxus/TwitterCorona with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lukxus/TwitterCorona")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lukxus/TwitterCorona") model = AutoModelForSequenceClassification.from_pretrained("lukxus/TwitterCorona", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from lukxus/TwitterCorona: direct link, hf CLI and curl.
- Browser
- Download file 795 Bytes
-
https://huggingface.co/lukxus/TwitterCorona/resolve/main/README.md
- Command line
-
hf download hf://lukxus/TwitterCorona/README.md
-
curl -L -o README.md https://huggingface.co/lukxus/TwitterCorona/resolve/main/README.md
795 Bytes
metadata
metrics:
- f1
language:
- en
datasets:
- argilla/twitter-coronavirus
tags:
- generated_from_trainer
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 |
|---|---|---|---|---|
| 0.902700 | 1.0 | 0.704850 | 0.740065 | 0.749341 |
| 0.531000 | 2.0 | 0.689495 | 0.777677 | 0.786924 |
| 0.375200 | 3.0 | 0.585254 | 0.809506 | 0.816099 |