Instructions to use genki10/ASAP_FineTuningBERT_AugV5_k1_task1_organization_fold2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use genki10/ASAP_FineTuningBERT_AugV5_k1_task1_organization_fold2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="genki10/ASAP_FineTuningBERT_AugV5_k1_task1_organization_fold2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("genki10/ASAP_FineTuningBERT_AugV5_k1_task1_organization_fold2") model = AutoModelForSequenceClassification.from_pretrained("genki10/ASAP_FineTuningBERT_AugV5_k1_task1_organization_fold2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
ASAP_FineTuningBERT_AugV5_k1_task1_organization_fold2
This model is a fine-tuned version of bert-base-uncased on the None dataset.
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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|---|---|---|---|---|---|---|
| No log | 1.0 | 2 | 10.0000 | -0.0015 | 9.9997 | 3.1622 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.19.1
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Model tree for genki10/ASAP_FineTuningBERT_AugV5_k1_task1_organization_fold2
Base model
google-bert/bert-base-uncased