Text Classification
Transformers
PyTorch
TensorBoard
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use wnic00/hihu4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wnic00/hihu4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="wnic00/hihu4")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("wnic00/hihu4") model = AutoModelForSequenceClassification.from_pretrained("wnic00/hihu4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
hihu4
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: 0.8167
- Accuracy: 0.7268
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: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.3799 | 1.0 | 1648 | 0.8540 | 0.7218 |
| 0.3021 | 2.0 | 3296 | 0.9949 | 0.7165 |
| 0.3724 | 3.0 | 4944 | 0.8167 | 0.7268 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cpu
- Datasets 2.14.4
- Tokenizers 0.13.0
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