Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use RogerKam/roberta_RCADE_fine_tuned_sentiment_covid_news with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RogerKam/roberta_RCADE_fine_tuned_sentiment_covid_news with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RogerKam/roberta_RCADE_fine_tuned_sentiment_covid_news")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RogerKam/roberta_RCADE_fine_tuned_sentiment_covid_news") model = AutoModelForSequenceClassification.from_pretrained("RogerKam/roberta_RCADE_fine_tuned_sentiment_covid_news") - Notebooks
- Google Colab
- Kaggle
roberta_RCADE_fine_tuned_sentiment_covid_news
This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1662
- Accuracy: 0.9700
- F1 Score: 0.9700
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: 2
Training results
Framework versions
- Transformers 4.20.1
- Pytorch 1.10.0+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1
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