Text Classification
Transformers
PyTorch
TensorBoard
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use wnic00/hihu3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wnic00/hihu3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="wnic00/hihu3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("wnic00/hihu3") model = AutoModelForSequenceClassification.from_pretrained("wnic00/hihu3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
hihu3
This model is a fine-tuned version of citizenlab/twitter-xlm-roberta-base-sentiment-finetunned on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.7639
- Accuracy: 0.7314
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: 1e-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
- lr_scheduler_warmup_steps: 600
- num_epochs: 5.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7062 | 1.0 | 1648 | 0.6899 | 0.7165 |
| 0.6047 | 2.0 | 3296 | 0.6696 | 0.7271 |
| 0.5057 | 3.0 | 4944 | 0.6754 | 0.7341 |
| 0.4325 | 4.0 | 6592 | 0.7141 | 0.7305 |
| 0.4025 | 5.0 | 8240 | 0.7639 | 0.7314 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cpu
- Datasets 2.14.4
- Tokenizers 0.13.0
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