Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use Vasanth/bert_emo_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vasanth/bert_emo_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Vasanth/bert_emo_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Vasanth/bert_emo_classifier") model = AutoModelForSequenceClassification.from_pretrained("Vasanth/bert_emo_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - emotion | |
| model-index: | |
| - name: bert_emo_classifier | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # bert_emo_classifier | |
| This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the emotion dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.2748 | |
| ## 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: 8 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 4 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 0.9063 | 0.25 | 500 | 0.4845 | | |
| | 0.3362 | 0.5 | 1000 | 0.3492 | | |
| | 0.2759 | 0.75 | 1500 | 0.2819 | | |
| | 0.2521 | 1.0 | 2000 | 0.2464 | | |
| | 0.1705 | 1.25 | 2500 | 0.2345 | | |
| | 0.1841 | 1.5 | 3000 | 0.2013 | | |
| | 0.1428 | 1.75 | 3500 | 0.1926 | | |
| | 0.1747 | 2.0 | 4000 | 0.1866 | | |
| | 0.1082 | 2.25 | 4500 | 0.2302 | | |
| | 0.1142 | 2.5 | 5000 | 0.2118 | | |
| | 0.1205 | 2.75 | 5500 | 0.2318 | | |
| | 0.1135 | 3.0 | 6000 | 0.2306 | | |
| | 0.0803 | 3.25 | 6500 | 0.2625 | | |
| | 0.0745 | 3.5 | 7000 | 0.2850 | | |
| | 0.085 | 3.75 | 7500 | 0.2719 | | |
| | 0.0701 | 4.0 | 8000 | 0.2748 | | |
| ### Framework versions | |
| - Transformers 4.15.0 | |
| - Pytorch 1.12.0+cu113 | |
| - Datasets 2.4.0 | |
| - Tokenizers 0.10.3 | |