Instructions to use MohammadKarami/bloom560mtask2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MohammadKarami/bloom560mtask2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MohammadKarami/bloom560mtask2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MohammadKarami/bloom560mtask2") model = AutoModelForSequenceClassification.from_pretrained("MohammadKarami/bloom560mtask2") - Notebooks
- Google Colab
- Kaggle
Model
This model is a fine-tuned version of bigscience/bloom-560m on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.3562
- F1: 0.7886
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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.9849 | 1.0 | 2500 | 0.8808 | 0.6951 |
| 0.4775 | 2.0 | 5000 | 0.8355 | 0.739 |
| 0.1347 | 3.0 | 7500 | 1.3562 | 0.7886 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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Model tree for MohammadKarami/bloom560mtask2
Base model
bigscience/bloom-560m