Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use autoevaluate/multi-class-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autoevaluate/multi-class-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="autoevaluate/multi-class-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("autoevaluate/multi-class-classification") model = AutoModelForSequenceClassification.from_pretrained("autoevaluate/multi-class-classification") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - emotion | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: multi-class-classification | |
| results: | |
| - task: | |
| name: Text Classification | |
| type: text-classification | |
| dataset: | |
| name: emotion | |
| type: emotion | |
| args: default | |
| metrics: | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.928 | |
| - task: | |
| type: text-classification | |
| name: Text Classification | |
| dataset: | |
| name: emotion | |
| type: emotion | |
| config: default | |
| split: test | |
| metrics: | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.9185 | |
| verified: true | |
| - name: Precision Macro | |
| type: precision | |
| value: 0.8738350796775306 | |
| verified: true | |
| - name: Precision Micro | |
| type: precision | |
| value: 0.9185 | |
| verified: true | |
| - name: Precision Weighted | |
| type: precision | |
| value: 0.9179425177997311 | |
| verified: true | |
| - name: Recall Macro | |
| type: recall | |
| value: 0.8650962919021573 | |
| verified: true | |
| - name: Recall Micro | |
| type: recall | |
| value: 0.9185 | |
| verified: true | |
| - name: Recall Weighted | |
| type: recall | |
| value: 0.9185 | |
| verified: true | |
| - name: F1 Macro | |
| type: f1 | |
| value: 0.8692821860210945 | |
| verified: true | |
| - name: F1 Micro | |
| type: f1 | |
| value: 0.9185 | |
| verified: true | |
| - name: F1 Weighted | |
| type: f1 | |
| value: 0.9181177508591364 | |
| verified: true | |
| - name: loss | |
| type: loss | |
| value: 0.20905950665473938 | |
| verified: true | |
| - name: matthews_correlation | |
| type: matthews_correlation | |
| value: 0.8920254536671932 | |
| verified: true | |
| <!-- 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. --> | |
| # multi-class-classification | |
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.2009 | |
| - Accuracy: 0.928 | |
| ## 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: 1 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 0.2643 | 1.0 | 1000 | 0.2009 | 0.928 | | |
| ### Framework versions | |
| - Transformers 4.19.2 | |
| - Pytorch 1.11.0+cu113 | |
| - Datasets 2.2.2 | |
| - Tokenizers 0.12.1 | |