Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use ueb1/IceBERT-finetuned-grouped with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ueb1/IceBERT-finetuned-grouped with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ueb1/IceBERT-finetuned-grouped")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ueb1/IceBERT-finetuned-grouped") model = AutoModelForSequenceClassification.from_pretrained("ueb1/IceBERT-finetuned-grouped") - Notebooks
- Google Colab
- Kaggle
IceBERT-finetuned-grouped
This model is a fine-tuned version of vesteinn/IceBERT on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.5660
- Accuracy: 0.2259
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 269 | 4.1727 | 0.1172 |
| 4.3535 | 2.0 | 538 | 3.8406 | 0.1632 |
| 4.3535 | 3.0 | 807 | 3.6718 | 0.2113 |
| 3.6711 | 4.0 | 1076 | 3.5660 | 0.2259 |
| 3.6711 | 5.0 | 1345 | 3.5332 | 0.2176 |
Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
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