Instructions to use rorschach-40/home-batch_3_2000_-text-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rorschach-40/home-batch_3_2000_-text-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rorschach-40/home-batch_3_2000_-text-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rorschach-40/home-batch_3_2000_-text-classification") model = AutoModelForSequenceClassification.from_pretrained("rorschach-40/home-batch_3_2000_-text-classification") - Notebooks
- Google Colab
- Kaggle
home-batch_3_2000_-text-classification
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4922
- Precision: 0.8654
- Recall: 0.9375
- F1: 0.9
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: 0.0003
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|---|---|---|---|---|---|---|
| No log | 1.0 | 25 | 0.4399 | 0.8776 | 0.8958 | 0.8866 |
| No log | 2.0 | 50 | 0.4922 | 0.8654 | 0.9375 | 0.9 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
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