Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use KingAsiedu/tweet_sentiments_analysis_roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KingAsiedu/tweet_sentiments_analysis_roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KingAsiedu/tweet_sentiments_analysis_roberta")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KingAsiedu/tweet_sentiments_analysis_roberta") model = AutoModelForSequenceClassification.from_pretrained("KingAsiedu/tweet_sentiments_analysis_roberta") - Notebooks
- Google Colab
- Kaggle
tweet_sentiments_analysis_roberta
This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6561
- F1-score: 0.6923
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: 5e-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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1-score |
|---|---|---|---|---|
| 0.7145 | 1.0 | 1000 | 0.6561 | 0.6923 |
| 0.5824 | 2.0 | 2000 | 0.6652 | 0.7270 |
| 0.4976 | 3.0 | 3000 | 0.7107 | 0.7620 |
| 0.3841 | 4.0 | 4000 | 0.8616 | 0.7777 |
| 0.2911 | 5.0 | 5000 | 1.0504 | 0.7748 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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Model tree for KingAsiedu/tweet_sentiments_analysis_roberta
Base model
FacebookAI/roberta-base