Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use siddharthgowda/roberta-tweet-virality-classification-full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use siddharthgowda/roberta-tweet-virality-classification-full with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="siddharthgowda/roberta-tweet-virality-classification-full")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("siddharthgowda/roberta-tweet-virality-classification-full") model = AutoModelForSequenceClassification.from_pretrained("siddharthgowda/roberta-tweet-virality-classification-full", device_map="auto") - Notebooks
- Google Colab
- Kaggle
COMS4705-roberta-tweet-virality-full-vfinal-last-minuete
This model is a fine-tuned version of siddharthgowda/COMS4705-roberta-tweet-virality-head-only-vfinal on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3471
- Accuracy: 0.8363
- F1: 0.9019
- Precision: 0.9875
- Recall: 0.8363
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-07
- train_batch_size: 256
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.4624 | 1.0 | 365 | 0.3623 | 0.8374 | 0.9025 | 0.9874 | 0.8374 |
| 0.4773 | 2.0 | 730 | 0.3470 | 0.8394 | 0.9037 | 0.9875 | 0.8394 |
| 0.4639 | 3.0 | 1095 | 0.3471 | 0.8363 | 0.9019 | 0.9875 | 0.8363 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
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