Instructions to use maddiehope/airlinetweets with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use maddiehope/airlinetweets with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="maddiehope/airlinetweets")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("maddiehope/airlinetweets") model = AutoModelForSequenceClassification.from_pretrained("maddiehope/airlinetweets", device_map="auto") - Notebooks
- Google Colab
- Kaggle
airlinetweets
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6723
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.5255 | 1.0 | 641 | 0.4095 |
| 0.3334 | 2.0 | 1282 | 0.4872 |
| 0.2082 | 3.0 | 1923 | 0.6723 |
Framework versions
- Transformers 4.34.1
- Pytorch 1.12.1
- Datasets 2.14.6
- Tokenizers 0.14.1
- Downloads last month
- 6
Model tree for maddiehope/airlinetweets
Base model
google-bert/bert-base-cased