Instructions to use NTCAL/norbert2_sentiment_norec_7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NTCAL/norbert2_sentiment_norec_7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NTCAL/norbert2_sentiment_norec_7")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NTCAL/norbert2_sentiment_norec_7") model = AutoModelForSequenceClassification.from_pretrained("NTCAL/norbert2_sentiment_norec_7") - Notebooks
- Google Colab
- Kaggle
norbert2_sentiment_norec_7
This model is a fine-tuned version of bert-large-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4995
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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.3162 | 1.0 | 5 | 0.6862 |
| 0.9102 | 2.0 | 10 | 0.8575 |
| 0.3545 | 3.0 | 15 | 0.5085 |
| 0.4631 | 4.0 | 20 | 0.4995 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
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