Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use AndrewDOrlov/bert_prof_single_128_below_100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AndrewDOrlov/bert_prof_single_128_below_100 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AndrewDOrlov/bert_prof_single_128_below_100")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AndrewDOrlov/bert_prof_single_128_below_100") model = AutoModelForSequenceClassification.from_pretrained("AndrewDOrlov/bert_prof_single_128_below_100", device_map="auto") - Notebooks
- Google Colab
- Kaggle
bert_prof_single_128_below_100
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6716
- Accuracy: 0.8674
- F1: 0.8658
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 | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.7447 | 1.0 | 6885 | 0.6995 | 0.8267 | 0.8151 |
| 0.5834 | 2.0 | 13770 | 0.6181 | 0.8436 | 0.8391 |
| 0.4465 | 3.0 | 20655 | 0.5877 | 0.8585 | 0.8551 |
| 0.3559 | 4.0 | 27540 | 0.6168 | 0.8638 | 0.8616 |
| 0.2515 | 5.0 | 34425 | 0.6300 | 0.8710 | 0.8692 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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