Instructions to use dianamihalache27/bertweetB_3epoch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dianamihalache27/bertweetB_3epoch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dianamihalache27/bertweetB_3epoch")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dianamihalache27/bertweetB_3epoch") model = AutoModelForSequenceClassification.from_pretrained("dianamihalache27/bertweetB_3epoch") - Notebooks
- Google Colab
- Kaggle
bertweetB_3epoch
This model is a fine-tuned version of vinai/bertweet-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1293
- Accuracy: 0.8571
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
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: 8
- eval_batch_size: 8
- 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 | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 434 | 0.1268 | 0.8571 | 0.0 | 0.0 | 0.0 |
| 0.195 | 2.0 | 868 | 0.1294 | 0.8571 | 0.0 | 0.0 | 0.0 |
| 0.1591 | 3.0 | 1302 | 0.1293 | 0.8571 | 0.0 | 0.0 | 0.0 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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Model tree for dianamihalache27/bertweetB_3epoch
Base model
vinai/bertweet-base