Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use DrinkIcedT/roberta-large_MBTI_pub_N with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DrinkIcedT/roberta-large_MBTI_pub_N with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DrinkIcedT/roberta-large_MBTI_pub_N")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DrinkIcedT/roberta-large_MBTI_pub_N") model = AutoModelForSequenceClassification.from_pretrained("DrinkIcedT/roberta-large_MBTI_pub_N", device_map="auto") - Notebooks
- Google Colab
- Kaggle
roberta-large_MBTI_N_MBTI_agg_balanced_50_1e-05_0.05
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 4.5828
- F1: 0.6338
- Threshold: 0.78
- F1 At 05: 0.6293
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
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 400
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Threshold | F1 At 05 |
|---|---|---|---|---|---|---|
| 2.0906 | 0.3175 | 200 | 2.5589 | 0.5257 | 0.58 | 0.4946 |
| 2.0985 | 0.6349 | 400 | 2.5012 | 0.5508 | 0.66 | 0.4837 |
| 1.9811 | 0.9524 | 600 | 2.4819 | 0.5821 | 0.52 | 0.5771 |
| 1.8486 | 1.2698 | 800 | 2.3223 | 0.6170 | 0.49 | 0.6159 |
| 1.7406 | 1.5873 | 1000 | 2.2683 | 0.6259 | 0.47 | 0.6235 |
| 1.6671 | 1.9048 | 1200 | 2.2952 | 0.6400 | 0.4 | 0.6344 |
| 1.3457 | 2.2222 | 1400 | 2.6800 | 0.6337 | 0.55 | 0.6317 |
| 1.3207 | 2.5397 | 1600 | 2.5167 | 0.6467 | 0.32 | 0.6387 |
| 1.2441 | 2.8571 | 1800 | 2.5364 | 0.6488 | 0.36 | 0.6336 |
| 0.8700 | 3.1746 | 2000 | 3.6583 | 0.6334 | 0.62 | 0.6259 |
| 0.7937 | 3.4921 | 2200 | 3.6179 | 0.6387 | 0.4200 | 0.6356 |
| 0.7774 | 3.8095 | 2400 | 3.5918 | 0.6346 | 0.67 | 0.6311 |
| 0.4730 | 4.1270 | 2600 | 4.3798 | 0.6321 | 0.22 | 0.6312 |
| 0.5801 | 4.4444 | 2800 | 4.2505 | 0.6333 | 0.72 | 0.6263 |
| 0.5751 | 4.7619 | 3000 | 4.5828 | 0.6338 | 0.78 | 0.6293 |
Framework versions
- Transformers 5.3.0
- Pytorch 2.7.1
- Datasets 4.8.5
- Tokenizers 0.22.2
- Downloads last month
- 3