Text Classification
Transformers
PyTorch
TensorBoard
mpnet
Generated from Trainer
text-embeddings-inference
Instructions to use ilaria-oneofftech/ikitracs_mitigation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ilaria-oneofftech/ikitracs_mitigation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ilaria-oneofftech/ikitracs_mitigation")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ilaria-oneofftech/ikitracs_mitigation") model = AutoModelForSequenceClassification.from_pretrained("ilaria-oneofftech/ikitracs_mitigation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
ikitracs_mitigation
This model is a fine-tuned version of sentence-transformers/all-mpnet-base-v2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7245
- F1-score: 0.2177
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: 9.24e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | F1-score |
|---|---|---|---|---|
| 1.2983 | 1.0 | 207 | 1.0239 | 0.1101 |
| 1.0475 | 2.0 | 414 | 0.9027 | 0.1449 |
| 0.9095 | 3.0 | 621 | 0.8214 | 0.1646 |
| 0.7996 | 4.0 | 828 | 0.8772 | 0.1629 |
| 0.6585 | 5.0 | 1035 | 0.7504 | 0.2025 |
| 0.5473 | 6.0 | 1242 | 0.8324 | 0.2144 |
| 0.4423 | 7.0 | 1449 | 0.6799 | 0.2176 |
| 0.3741 | 8.0 | 1656 | 0.7245 | 0.2177 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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