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
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Download README.md from lukxus/TwitterCorona: direct link, hf CLI and curl.
- Browser
- Download file 795 Bytes
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https://huggingface.co/lukxus/TwitterCorona/resolve/main/README.md
- Command line
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hf download hf://lukxus/TwitterCorona/README.md
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curl -L -o README.md https://huggingface.co/lukxus/TwitterCorona/resolve/main/README.md
795 Bytes
| 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 | | |