Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use AndrewDOrlov/bert_for_prof_roles_128_all_labels with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AndrewDOrlov/bert_for_prof_roles_128_all_labels with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AndrewDOrlov/bert_for_prof_roles_128_all_labels")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AndrewDOrlov/bert_for_prof_roles_128_all_labels") model = AutoModelForSequenceClassification.from_pretrained("AndrewDOrlov/bert_for_prof_roles_128_all_labels", device_map="auto") - Notebooks
- Google Colab
- Kaggle
bert_for_prof_roles_128_all_labels
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0059
- F1: 0.8558
- Roc Auc: 0.9126
- Accuracy: 0.8233
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: 2e-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 | Roc Auc | Accuracy |
|---|---|---|---|---|---|---|
| 0.0119 | 1.0 | 7016 | 0.0108 | 0.7427 | 0.8192 | 0.6327 |
| 0.0076 | 2.0 | 14032 | 0.0077 | 0.8118 | 0.8790 | 0.7549 |
| 0.006 | 3.0 | 21048 | 0.0067 | 0.8352 | 0.8958 | 0.7874 |
| 0.005 | 4.0 | 28064 | 0.0062 | 0.8459 | 0.9058 | 0.8074 |
| 0.0046 | 5.0 | 35080 | 0.0060 | 0.8525 | 0.9112 | 0.8190 |
Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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