Instructions to use Tanor/BERTicSENTPOS0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tanor/BERTicSENTPOS0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tanor/BERTicSENTPOS0")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Tanor/BERTicSENTPOS0") model = AutoModelForSequenceClassification.from_pretrained("Tanor/BERTicSENTPOS0") - Notebooks
- Google Colab
- Kaggle
BERTicSENTPOS0
This model is a fine-tuned version of Tanor/BERTicSENTPOS0 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0233
- F1: 0.7692
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: 64
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 32
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| No log | 1.0 | 47 | 0.0200 | 0.6364 |
| No log | 2.0 | 94 | 0.0311 | 0.5333 |
| No log | 3.0 | 141 | 0.0305 | 0.5333 |
| No log | 4.0 | 188 | 0.0171 | 0.7 |
| No log | 5.0 | 235 | 0.0200 | 0.6957 |
| No log | 6.0 | 282 | 0.0207 | 0.72 |
| No log | 7.0 | 329 | 0.0233 | 0.7692 |
Framework versions
- Transformers 4.40.1
- Pytorch 2.2.2
- Datasets 2.19.0
- Tokenizers 0.19.1
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