Instructions to use bkane2/skills-trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bkane2/skills-trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bkane2/skills-trainer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bkane2/skills-trainer") model = AutoModelForSequenceClassification.from_pretrained("bkane2/skills-trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
skills-trainer
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3105
- F1: 0.5057
- Roc Auc: 0.7025
- Accuracy: 0.7153
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: 5e-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: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|---|---|---|---|---|---|---|
| No log | 1.0 | 360 | 0.2976 | 0.4 | 0.6366 | 0.6917 |
| 0.3239 | 2.0 | 720 | 0.2877 | 0.4625 | 0.6740 | 0.7097 |
| 0.2078 | 3.0 | 1080 | 0.3105 | 0.5057 | 0.7025 | 0.7153 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.10.0
- Tokenizers 0.13.2
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