Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use slickdata/finetuned-Sentiment-classfication-ROBERTA-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use slickdata/finetuned-Sentiment-classfication-ROBERTA-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="slickdata/finetuned-Sentiment-classfication-ROBERTA-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("slickdata/finetuned-Sentiment-classfication-ROBERTA-model") model = AutoModelForSequenceClassification.from_pretrained("slickdata/finetuned-Sentiment-classfication-ROBERTA-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| base_model: roberta-base | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: finetuned-Sentiment-classfication-ROBERTA-model | |
| 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. --> | |
| # finetuned-Sentiment-classfication-ROBERTA-model | |
| This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.2222 | |
| - Rmse: 0.2936 | |
| ## 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: 3e-05 | |
| - train_batch_size: 4 | |
| - eval_batch_size: 4 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 16 | |
| - total_train_batch_size: 64 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 500 | |
| - num_epochs: 16 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Rmse | | |
| |:-------------:|:-----:|:----:|:---------------:|:------:| | |
| | 0.6684 | 2.72 | 500 | 0.3931 | 0.4892 | | |
| | 0.1963 | 5.43 | 1000 | 0.2222 | 0.2936 | | |
| | 0.0755 | 8.15 | 1500 | 0.2479 | 0.2757 | | |
| | 0.0413 | 10.86 | 2000 | 0.3233 | 0.2794 | | |
| | 0.0213 | 13.58 | 2500 | 0.3590 | 0.2689 | | |
| ### Framework versions | |
| - Transformers 4.31.0 | |
| - Pytorch 2.0.1+cu118 | |
| - Datasets 2.13.1 | |
| - Tokenizers 0.13.3 | |