Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use AndrewDOrlov/bert_prof_single_v2_128_below_100_60_40 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AndrewDOrlov/bert_prof_single_v2_128_below_100_60_40 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AndrewDOrlov/bert_prof_single_v2_128_below_100_60_40")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AndrewDOrlov/bert_prof_single_v2_128_below_100_60_40") model = AutoModelForSequenceClassification.from_pretrained("AndrewDOrlov/bert_prof_single_v2_128_below_100_60_40", device_map="auto") - Notebooks
- Google Colab
- Kaggle
bert_prof_single_v2_128_below_100_60_40
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6731
- Accuracy: 0.8571
- F1: 0.8541
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 |
|---|---|---|---|---|---|
| 0.7825 | 1.0 | 6004 | 0.7795 | 0.8098 | 0.7965 |
| 0.6343 | 2.0 | 12008 | 0.6605 | 0.8361 | 0.8294 |
| 0.4935 | 3.0 | 18012 | 0.6383 | 0.8467 | 0.8429 |
| 0.3808 | 4.0 | 24016 | 0.6569 | 0.8540 | 0.8507 |
| 0.272 | 5.0 | 30020 | 0.6713 | 0.8562 | 0.8529 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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